diff --git a/index-preview.html b/index-preview.html deleted file mode 100644 index 3d9122a..0000000 --- a/index-preview.html +++ /dev/null @@ -1,3617 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - VLMs are Blind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- Vision language models are blind -

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- *Equal contribution -
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- 1Auburn University, - 2University of Alberta, -
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Abstract

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- Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 - Pro are - powering countless image-text processing applications and scoring high on existing vision-understanding - benchmarks. - Yet, we find that VLMs fail on 7 visual tasks absurdly easy to humans such as identifying (a) - whether two - circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word; and (d) - counting the - number of circles in an Olympic-like logo. - The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like that - of a person - with myopia seeing fine details as - blurry, and at - worst, like an intelligent person who is blind making - educated - guesses. -

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Task 1: Counting line intersections Two intersecting lines

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- Given the impressive accuracy of VLMs on answering questions on diagrams and charts (e.g., Sonnet-3.5 scoring 94.7% on AI2D and 90.8% on - ChartQA) [1], a reasonable hypothesis is that VLMs must be able - to see whether two graphs - intersect in a - chart. Here, we test this hypothesis by asking VLMs to count the number of intersections between two 2-segment - piece-wise linear functions. -

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Images

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- We create 150 images (see Figure 1) of 2D line plots drawn on a white canvas. Each line plot consists of two - line - segments, defined by three points whose x-coordinates are fixed and equally spaced. The y-coordinates are - randomly - sampled to create two plots that intersect at exactly 0, 1 or 2 points. See Appendix A for more details. -

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- 2D line plot example 1 -
0 intersections
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- 2D line plot example 2 -
1 intersection
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- 2D line plot example 3 -
2 intersections
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- 2D line plot example 4 -
2 intersections
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Fig. 1: Examples of 2D line plots used in the task, showing different numbers of intersections. -
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Prompts

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- We ask each question using two different wordings: -

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  1. "How many times do the blue and red line plots cross each other?"
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  3. "How many times do the blue and red lines intersect?"
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Groundtruth

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- Answers are ∈ {0, 1, 2} (random-baseline accuracy: 33%). -

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Results

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- The following table shows the performance of the four models on the task of counting line intersections. -

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Thickness -
- GPT-4o -
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- Gemini-1.5 Pro -
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- Sonnet-3 -
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- Sonnet-3.5 -
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245.0070.0064.0080.00
347.0068.0066.0079.00
454.0071.0062.0073.00
Average48.6769.6764.0077.33
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Qualitative samples

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How many times do the blue and red lines intersect?

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Graph 1Graph 2Graph 3Graph 4Graph 5Graph 6
1✗ - 1✗ - 2✗ - 2✓ - 2✓ - 1✗ -
1✗ - 1✗ - 1✓ - 1✗ - 1✗ - 1✗ -
1✗ - 1✗ - 2✗ - 1✗ - 1✗ - 1✗ -
1✗ - 0✓ - 2✗ - 1✗ - 1✗ - 2✓ -
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Fig. 2: VLMs cannot reliably count the intersections.
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Task 2: Two circles Two intersecting lines

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- In contrast to Task 1 where we tested VLMs on thin lines, here we evaluate their ability to perceive - interactions - between larger objects - specifically, two same-sized filled circles. This task assesses VLMs' capability to - detect - (1) small gaps between circles and (2) overlapping circles. -

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Images

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- We generate 672 images of two circles on a white canvas. The circles vary in size, distance, and orientation: -

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  • Circle diameters: 1/4, 1/5, 1/6, or 1/7 of the canvas size
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  • Distances between circle perimeters: -0.15 to 0.5 times the diameter
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  • Orientations: 90°, 0°, -45°, and 45° angles with the x-axis
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  • Canvas sizes: 384, 769, and 1155 pixels
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- Overlapping circles -
Overlapping and touching
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- Touching circles -
Non-overlapping but touching
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- Separated circles -
Non-overlapping and non-touching
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- Diagonal orientation -
Different orientation
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Fig. 3: Examples of two-circle images used in the task, showing different configurations. -
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Prompts

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- We ask each question using two different wordings: -

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  1. "Are the two circles touching each other? Answer with Yes/No."
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  3. "Are the two circles overlapping? Answer with Yes/No."
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Groundtruth

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- Answers are based on the distance d between circle perimeters: -

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  • d < 0: Overlapping and touching
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  • d = 0: Non-overlapping but touching
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  • d > 0: Non-overlapping and non-touching
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Random-baseline accuracy: 50%.

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Results

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- The following table shows the performance of the four models on the task of counting line intersections. -

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- GPT-4o -
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- Gemini-1.5 Pro -
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- Sonnet-3 -
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- Sonnet-3.5 -
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Overlapping71.2793.3088.0988.83
Touching74.1092.2680.9594.49
Average72.6992.7884.5291.66
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Qualitative samples

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Are the two circles overlapping? Answer with Yes/No.

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Circle 1Circle 2Circle 3Circle 4Circle 5Circle 6
Yes✓ - Yes✓ - Yes✗ - Yes✗ - No✓ - Yes✗ -
No✗ - Yes✓ - Yes✗ - No✓ - No✓ - No✓ -
Yes✓ - Yes✓ - Yes✗ - Yes✗ - Yes✗ - No✓ -
No✗ - No✗ - No✓ - No✓ - No✓ - No✓ -
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Fig. 4: VLMs consistently fail at smaller distances. However, at a large gap, GPT-4o remains unreliable (rightmost). Sonnet-3.5 tends to conservatively answer "No" - regardless of the actual distance - between the two circles.
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Task 3: The circled letter Two intersecting lines

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- Consistent with prior reports [2][3][4], we find - that VLMs can - 100% - accurately identify a primitive shape (e.g., a red circle â­•)[2] - and can perfectly read an English word (e.g., - Subdermatoglyphic) alone. Here, we superimposed the red circle on every - letter, one at a time, in the word, - and ask - VLMs to identify which letter is being circled. While the task is easy to humans, our hypothesis is that if a - VLM's - vision is "blurry", it might not be able to identify the exact letter being circled since there is tiny - spacing - between the adjacent letters. -

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Images

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- We choose three strings Acknowledgement, Subdermatoglyphic, and tHyUiKaRbNqWeOpXcZvM because they contain - characters - of variable widths and heights. Furthermore, all four tested VLMs can read out all characters in these strings - when - they are input to the models as an image. While Acknowledgement is a common - English word, Subdermatoglyphic is - the - longest word without repetitive letters. We also test VLMs on the random string tHyUiKaRbNqWeOpXcZvM to - estimate how - much model accuracy is due to its familiarity with the word. -

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- For each (string, circled-letter) pair, we render a 512×512 image by choosing among 3 red oval line-thickness - levels, - 2 font sizes, and 4 random positions in the canvas for a total of 24 images. That is, we generate 360, 408, - and 480 - images for Acknowledgement (15 letters), Subdermatoglyphic (17 letters), and - tHyUiKaRbNqWeOpXcZvM (20 - letters), - respectively. We ensure each letter to be circled fits completely the oval. -

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- Circled letter example 1 -
Acknowledgement with 'n' circled
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- Circled letter example 2 -
tHyUiKaRbNqWeOpXcZvM with 't' circled
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- Circled letter example 3 -
tHyUiKaRbNqWeOpXcZvM with 'X' circled
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- Circled letter example 4 -
Subdermatoglyphic with 'u' circled
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Fig. 5: Examples of circled letter images used in the task, showing different words and circled - letters. -
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Prompts

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- We ask each question using two different wordings: -

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  1. "Which letter is being circled?"
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  3. "Which character is being highlighted with a red oval?"
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Groundtruth

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- Letters need to match predicted letters exactly (case-insensitive). -

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Results

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- The following table shows the performance of the four models on the task of identifying the circled letter. -

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Word -
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Acknowledgement69.0397.5082.6491.11
Subdermatoglyphic63.6091.0571.4594.49
tHyUiKaRbNqWeOpXcZvM77.9289.9065.9482.08
Average70.1892.8173.3489.22
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Qualitative samples

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Which letter is being circled?

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Circled Letter 1Circled Letter 2Circled Letter 3Circled Letter 4Circled Letter 5Circled Letter 6
o✗ - e✗ - t✗ - o✗ - o✗ - z✗ -
w✗ - m✓ - n✓ - p✓ - o✗ - v✓ -
o✗ - e✗ - e✗ - y✗ - a✗ - t✗ -
l✓ - e✗ - t✗ - h✗ - t✓ - m✗ -
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Fig. 6: Identifying the letter being circled is non-trivial for VLMs across both English words - (Acknowledgement & Subdermatoglyphic) - and a random string - (tHyUiKaRbNqWeOpXcZvM). When making mistakes, VLMs - tend to - predict letters adjacent to the circled one.
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Task 4: Counting overlapping shapes Two intersecting lines

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- Aligned with prior research [4], we also find VLMs to be able - to count disjoint circles. Yet, here, we test - VLMs on - counting circles that are intersecting like in the Olympic logo—a common cognitive development - exercise for - preschoolers [5][6]. - Our hypothesis is that a "blurry" vision may not see the - intersection between two circles - clearly - and therefore unable to trace circles and count them. For generalization of our findings, we repeat the - experiment - with pentagons as well. -

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Images

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- In an image of size C×C, where C ∈ {384, 769, 1155}px, we draw N ∈ {5, 6, 7, 8, 9} overlapping, same-sized - circles - arranged in two rows like the Olympic logo. A circle diameter φ ∈ {C/5, C/10}. We repeat the images with two - different - line thickness for rendering circles. This procedure renders 3 resolutions × 5 × 2 diameters = 60 images. We - repeat - for pentagons in addition to circles, resulting in 60 × 2 shapes = 120 images in total. For pentagons, their - side - length d ∈ {C/5, C/10}. -

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- Olympic-like logo example 1 -
5 circles, small diameter
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- Olympic-like logo example 2 -
6 circles, large diameter
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- Olympic-like logo example 3 -
8 colored circles
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- Olympic-like logo example 4 -
9 colored pentagons
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Fig. 7: Examples of Olympic-like logo images used in the task, showing different numbers of shapes, - sizes, - and colors.
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Prompts

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- We ask each question using two different wordings: -

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  1. "How many {shapes} are in the image? Answer with only the number in numerical format."
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  3. "Count the {shapes} in the image. Answer with a number in curly brackets e.g. {3}."
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- Where {shapes} is either "circles" or "pentagons" depending on the image. -

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Groundtruth

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- Answers are ∈ {5, 6, 7, 8, 9} (random-baseline accuracy: 20%). -

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Results

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- The following table shows the performance of the four models on the task of identifying the circled letter. -

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Circles42.5020.8331.6644.16
Pentagons19.169.1611.6675.83
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Qualitative samples

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How many circles are in the image? Answer with only the number in numerical format.

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Circle 1Circle 2Circle 3Circle 4Circle 5Circle 6
5✗6✓5✗10✗10✗5✗
5✗5✗5✗5✗5✗5✗
5✗5✗5✗10✗10✗5✗
5✗6✓6✓10✗9✓7✓
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Fig. 8: Gemini-1.5 - Pro often predicts "5" circles.
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Task 5: Counting the nested squares Two intersecting lines

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- Motivated by the findings that VLMs struggle in counting the intersected circles (Task 4), here, we arrange - the shapes - differently so that their edges do not intersect. - That is, each shape is nested entirely inside another. For completeness, we test squares in this task. -

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Images

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- In a canvas of size C×C, we render N ∈ {2, 3, 4, 5} nested squares. - The outermost square is rendered first using a random edge length d and a line thickness ∈ {2, 3, 4}px. - The remaining N-1 squares are drawn using a size reduction factor, 0.75 × d and placed at a random coordinate - that - ensures they do not touch outer squares. - For each line thickness, we generate 10 images (where squares have different, random locations) to create 3 × - 10 = 30 - images. - Repeating the process for all N values results in 4 × 30 = 120 images. -

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- 2 nested squares -
2 nested squares
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- 3 nested squares -
3 nested squares
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4 nested squares
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5 nested squares
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Fig. 9: Examples of nested square images used in the task, showing different numbers of squares. -
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Prompts

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- We ask each question using the following wording: -

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  1. "Count the total number of squares in the image."
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- Where {shapes} is either "circles" or "pentagons" depending on the image. -

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Groundtruth

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- Answers are ∈ {2, 3, 4, 5} (random-baseline accuracy: 25%). -

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Results

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- The following table shows the performance of the four models on the task of counting nested squares. -

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- GPT-4o -
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- Gemini-1.5 Pro -
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- Sonnet-3 -
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Squares48.3380.0055.0087.50
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Qualitative samples

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Count total number of squares in the image.

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Nested Squares 1Nested Squares 2Nested Squares 3Nested Squares 4Nested Squares 5Nested Squares 6
5✗5✗5✗5✗6✗6✗
5✗5✗5✗5✗5✓5✓
5✗5✗5✗5✗4✗4✗
4✓4✓4✓4✓4✗4✗
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Fig. 10: Only Sonnet-3.5 can count - the - squares in a majority of the images.
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Task 6: Counting the rows and columns of a grid Two intersecting lines

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- The results from prior tasks show VLMs cannot always count shapes that are overlapping (Task 4) or nested - (Task 5). - What about adjacent shapes? Here, we tile up shapes (specifically, squares) into a grid and challenge VLMs to - count—a - task that is supposedly simple to VLMs given their remarkable performance (≥ 90% accuracy) on DocVQA, which - includes - many questions with tables. - To simplify the task, we ask models to count the number of rows and columns in a given table. -

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Images

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- A grid may have N×N, N×N', or N'×N cells, where N∈{3, 4, 5, 6, 7, 8, 9} and N' = N + 1. - Each grid is rendered with two different line-thicknesses on a canvas of size C×C where C∈{500, 1250, 2000}px. - Besides empty grids, we also replicate the procedure to make grids contain text (which is more common in - real-world - tables) where each cell contains a single random word. - Two versions combined have 2×222 = 444 images. -

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- Text grid 3x3 -
Text grid (3x3)
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Text grid (3x4)
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Empty grid (4x4)
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Empty grid (4x5)
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Fig. 9: Examples of grid images used in the task, showing text-filled and empty grids with various - dimensions.
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Prompts

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- We ask each question using two different wordings: -

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  1. "Count the number of rows and columns and answer with numbers in curly brackets. For example, rows={5} - columns={6}"
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  3. "How many rows and columns are in the table? Answer with only the numbers in a pair (row, column), - e.g., - (5,6)"
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Groundtruth

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- Answers include both the number of rows and columns. An answer is correct when both column and row counts - are - correctly predicted. -

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Results

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- The following table shows the performance of the four models on the task of counting rows and columns in - grids. -

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Grid type -
- GPT-4o -
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- Sonnet-3 -
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- Sonnet-3.5 -
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Blank26.1325.7525.0059.84
Text53.0345.8347.3488.68
Average39.5835.7936.1774.26
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Qualitative samples

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Count the number of rows and columns and answer with numbers in curly brackets. For example, rows={5} - columns={6} -

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Grid 1 - Grid 2 - Grid 3 - Grid 4 - Grid 5 - Grid 6 -
4×4✗ - 6×6✗ - 7×7✗ - 6×6✗ - 6×6✗ - 6×6✗ -
5×5✗ - 6×6✗ - 7×7✗ - 10×10✗ - 5×6✓ - 10×10✗ -
5×5✗ - 7×8✗ - 6×6✗ - 9×9✗ - 6×6✗ - 9×12✗ -
4×5✓ - 6×7✓ - 7×7✗ - 8×7✓ - 5×6✓ - 8×8✗ -
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Fig. 12: Examples from the benchmark show that models consistently fail at counting rows and - columns of - blank grids.
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How many rows and columns are in the table? Answer with only the numbers in a pair (row, column), - e.g., (5,6).

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Grid 1 - Grid 2 - Grid 3 - Grid 4 - Grid 5 - Grid 6 -
4×4✓ - 4×5✓ - 5×4✓ - 5×6✓ - 6×8✗ - 7×8✗ -
4×4✓ - 4×5✓ - 5×4✓ - 5×6✓ - 6×8✗ - 7×8✗ -
4×4✓ - 5×5✗ - 5×4✓ - 6×6✗ - 7×7✗ - 8×7✗ -
4×4✓ - 4×5✓ - 5×4✓ - 5×6✓ - 6×7✓ - 7×7✓ -
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Fig. 13: When text is included in the cells of the grid, the performance of all VLMs improves, - especially - Sonnet-3.5. -
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Task 7: Following single-colored paths Two intersecting lines

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- It is important for VLMs to be able to follow paths in order to read maps or charts, interpret graphs, and - understand - user notations (e.g., arrows) in input images. To assess path-following capability, this task asks models to - count the - unique-color paths between two given stations in a simplified subway map. This is another easy-to-humans task - that - challenges VLMs significantly. -

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Images

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- We create each subway map on an image of size C×C, where C ∈ {512, 1024}px. We write 4 station names (A, B, C, - D) at 4 - fixed coordinates. We divide the canvas into an invisible grid of 18×18 cells and initialize 3 path-starting - points - C/18px away from each station. We draw a path, using the depth-first search algorithm starting from a random - station - and a random starting point, where a valid move is one cell in any direction: North, south, east or west. We - repeat - the process so that each station has exactly N ∈ {1, 2, 3} outgoing paths, for a total of 180 maps. -

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- Station with 1 path -
1 path, 10px width
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- Station with 2 paths -
2 paths, 20px width
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- Station with 2 paths -
2 paths, 20px width
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-
-
- Station with 3 paths -
3 paths, 10px width
-
-
-
-
Fig. 14: Examples of subway map images used in the task, showing different numbers of paths and - variations - in path thickness.
-
- -

Prompts

-
-

- We ask each question using two different wordings: -

-
    -
  1. "How many single-colored paths go from A to C? Answer with a number in curly brackets, e.g., {3}" -
  2. -
  3. "Count the one-colored routes that go from A to C. Answer with a number in curly brackets, e.g., - {3}." -
  4. -
- -

Groundtruth

-
-

- Answers are ∈ {0, 1, 2, 3} (random-baseline accuracy: 25%). -

-
- -

Results

-

- The following table shows the performance of the four models on the task of counting single-colored paths - between - stations. -

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Paths -
- GPT-4o -
-
-
- Gemini-1.5 Pro -
-
-
- Sonnet-3 -
-
-
- Sonnet-3.5 -
-
167.5085.4123.7595.00
244.3728.7537.1856.25
336.7125.7815.4225.39
Average45.8940.0123.7850.18
-
-
-

Qualitative samples

-
-
-
-

How many single-color paths go from A to D? Answer with a number in curly brackets e.g. {3}

-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Subway Map 1Subway Map 2Subway Map 3Subway Map 4Subway Map 5Subway Map 6
1✓ - 1✓ - 2✗ - 3✗ - 2✗ - 1✗ -
2✗ - 2✗ - 4✗ - 1✓ - 1✓ - 4✗ -
2✗ - 1✓ - 3✗ - 2✗ - 4✗ - 4✗ -
1✓ - 1✓ - 3✗ - 3✗ - 2✗ - 3✗ -
-
-
- GPT-4o GPT-4o -
-
- Gemini-1.5 Gemini-1.5 - Pro -
-
- Sonnet-3 Sonnet-3 -
-
- Sonnet-3 Sonnet-3.5 -
-
-
-
Fig. 15: Some VLMs (Gemini-1.5, - Sonnet-3) - surprisingly fail in even extremely easy cases (leftmost). - As the - number of paths exiting each station increases, VLMs tend to perform worse. -
- -
-
-
- - - - - - - - - - - \ No newline at end of file diff --git a/index.html b/index.html index 3d9122a..9607dab 100644 --- a/index.html +++ b/index.html @@ -13,8 +13,6 @@ - - @@ -1265,6 +1263,116 @@ font-size: 0.8em; } } + + + .image-container { + display: flex; + flex-direction: column; + height: 100%; + } + + .image-container img { + flex: 1; + object-fit: contain; + width: 100%; + height: auto; + max-height: 200px; + /* Adjust this value as needed */ + } + + .image-container figcaption { + text-align: center; + margin-top: 10px; + } + + .table-container { + overflow-x: auto; + } + + .performance-table { + width: 100%; + border-collapse: collapse; + } + + .performance-table th, + .performance-table td { + padding: 8px; + text-align: center; + } + + + .performance-table { + width: 100%; + border-collapse: collapse; + font-size: 0.9em; + /* Slightly reduce font size */ + } + + .performance-table th, + .performance-table td { + border: 1px solid #ddd; + padding: 4px; + /* Reduce padding */ + text-align: center; + } + + .task-icon-container { + display: flex; + justify-content: center; + align-items: center; + height: 30px; + /* Reduce height */ + } + + .task-icon-small { + width: 20px; + /* Slightly reduce icon size */ + height: 20px; + } + + .model-logo-small { + width: 16px; + /* Slightly reduce logo size */ + height: 16px; + vertical-align: middle; + } + + .performance-table .rnumber { + text-align: right; + padding-right: 2px; + /* Add a bit of padding on the right for numbers */ + } + + /* Add a class for narrow columns */ + .performance-table .narrow-column { + width: 8%; + /* Adjust this value as needed */ + } + + /* Make the first column (Model) slightly wider */ + .performance-table th:first-child, + .performance-table td:first-child { + width: 12%; + /* Adjust this value as needed */ + } + + /* Responsive design for smaller screens */ + @media screen and (max-width: 768px) { + .performance-table { + font-size: 0.8em; + } + + .performance-table th, + .performance-table td { + padding: 2px; + } + + .task-icon-small, + .model-logo-small { + width: 16px; + height: 16px; + } + } @@ -1369,21 +1477,24 @@

Abstract

Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 - Pro are - powering countless image-text processing applications and scoring high on existing vision-understanding - benchmarks. - Yet, we find that VLMs fail on 7 visual tasks absurdly easy to humans such as identifying (a) - whether two - circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word; and (d) - counting the - number of circles in an Olympic-like logo. - The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like that - of a person - with myopia seeing fine details as - blurry, and at - worst, like an intelligent person who is blind making - educated - guesses. + Pro + are powering various image-text applications and scoring high on many vision-understanding benchmarks. We + propose + BlindTest, a suite of 7 visual tasks very + easy to + humans such as identifying (a) whether two circles overlap; (b) whether two lines intersect; (c) which + letter is being + circled in a word; and (d) counting the number of circles in an Olympic-like logo. Surprisingly, four + state-of-the-art + VLMs are, on average, only 58.12% accurate on our benchmark where the human expected accuracy is 100%. + Sonnet-3.5 performs the best at 74.01% + accuracy. On BlindTest, VLMs struggle + with tasks that require precise + spatial information and simple counting (from 0 to 2, 2 to 5, or 5 to 10), sometimes providing an + impression of a + person with myopia seeing fine details + as blurry + and making educated guesses.

@@ -1460,38 +1571,37 @@

Task 1: Counting line intersections Images

- We create 150 images (see Figure 1) of 2D line plots drawn on a white canvas. Each line plot consists of two - line - segments, defined by three points whose x-coordinates are fixed and equally spaced. The y-coordinates are - randomly - sampled to create two plots that intersect at exactly 0, 1 or 2 points. See Appendix A for more details. + We create 1800 images of 2D line plots drawn on a white canvas. Each line plot consists of two + line segments, defined by three points whose x-coordinates are fixed and equally spaced. The y-coordinates are + randomly sampled to create two plots that intersect at exactly 0, 1 or 2 points. See Appendix A for more + details.

-
- + 2D line plot example 1
0 intersections
-
- + 2D line plot example 2
1 intersection
-
- + 2D line plot example 3 -
2 intersections
+
1 intersection
-
- + 2D line plot example 4
2 intersections
@@ -1506,8 +1616,12 @@

Prompts

We ask each question using two different wordings:

    -
  1. "How many times do the blue and red line plots cross each other?"
  2. -
  3. "How many times do the blue and red lines intersect?"
  4. +
  5. "How many times do the blue and red lines touch each other? Answer with a number in curly brackets, + e.g., + {5}."
  6. +
  7. "Count the intersection points where the blue and red lines meet. Put your answer in curly brackets, + e.g., + {2}."

Groundtruth

@@ -1517,9 +1631,6 @@

Groundtruth

- - -

Results

The following table shows the performance of the four models on the task of counting line intersections. @@ -1527,7 +1638,7 @@

Results

- + - + - - - - - - - - - - + + + - - - - - + + + + + - - - - - + + + + +
ThicknessLine width
GPT-4o @@ -1550,34 +1661,28 @@

Results

20.005 × C 45.0070.0064.0080.00
347.0068.0066.0079.0067.5545.2275.83
454.0071.0062.0073.000.01 × C38.2266.3341.6174.88
Average48.6769.6764.0077.33Mean41.6166.9443.4175.36
+
@@ -1590,24 +1695,24 @@

How many times do the blue and red lines intersect?

- - - - - - + + + + + + - - - - - -
Graph 1Graph 2Graph 3Graph 4Graph 5Graph 6Graph 1 + Graph 2 + Graph 3 + Graph 4 + Graph 5 + Graph 6 +
How many times do the blue and red lines intersect? 1✗ 1✗ + 0✓ 2✗ 2✓ + class="cross">✗ 2✓ + 4✗ 1✗ @@ -1643,7 +1748,7 @@

How many times do the blue and red lines intersect?

1✓ 12✗ 1How many times do the blue and red lines intersect?
14✗ 1How many times do the blue and red lines intersect? class="cross">✗ 1✗ + class="check">✓ 14✗ 1How many times do the blue and red lines intersect? class="cross">✗ 1✗ + class="check">✓ 13✗ 2How many times do the blue and red lines intersect?
-
GPT-4o GPT-How many circles are in the image? Answer with only the number in numerical - - - + - + alt="Circle 6"> + - + class="check">✓ + - - + class="cross">✗ + + + + + class="cross">✗ + + class="check">✓ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + - + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + - + + class="check">✓ + + class="check">✓ + + class="cross">✗ + + class="check">✓ + + class="check">✓ +
Circle 1Circle 2 Circle 3Circle 3 + Circle 4Circle 5 Circle 6
5✗6✓ 5✗10✗10✗7✗ + 12✗ + 11✗ + 5✗
5✗ 5✗ 5✗ 5✗ 5✗ 5✗
3✗ + 5✗5✗ 5✗ 10✗ 10✗ 5✗
5✗4✗ + 6✓ 6✓ 10✗ 9✓ 7✓
@@ -2685,7 +2819,11 @@

Prompts

We ask each question using the following wording:

    -
  1. "Count the total number of squares in the image."
  2. +
  3. "How many squares are in the image? Please answer with a number in curly brackets e.g., {10}." +
  4. +
  5. "Count total number of squares in the image. Answer with only the number in numerical format in curly + brackets + e.g. {3}."

Where {shapes} is either "circles" or "pentagons" depending on the image. @@ -2728,10 +2866,10 @@

Results

Squares - 48.33 - 80.00 - 55.00 - 87.50 + 55.83 + 87.08 + 65.00 + 92.08 @@ -2743,93 +2881,147 @@

Qualitative samples

-

Count total number of squares in the image.

+

How many squares are in the image? Please answer with a number in curly brackets e.g. {10}.

+ + + + class="cross">✗ + + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + class="cross">✗ + + + + + - - - + class="cross">✗ + + class="cross">✗ + + class="check">✓ + + + + + + - - - + class="cross">✗ + + class="check">✓ + + class="cross">✗ + + + + + + - - - + class="check">✓ + + class="check">✓ + + class="cross">✗ + +
Nested Squares 1 Nested Squares 2 Nested Squares 3 Nested Squares 4 Nested Squares 5 Nested Squares 6Nested Squares 7Nested Squares 8
3✗ + 5✗3✓ + 5✗ 5✗ 5✗ 6✗ 6✗
2✓ + 3✓ + 2✗ + 3✓ + 5✗5✗5✗5✗ 5✓ 5✓4✗ +
2✓ + 4✗ + 2✗ + 4✗ + 5✗5✗5✗5✗ 4✗ 4✗5✓ +
2✓ + 3✓ + 3✓ + 3✓ + 4✓4✓4✓4✓ 4✗ 4✗5✓ +
@@ -3411,31 +3603,31 @@

Results

1 - 67.50 - 85.41 - 23.75 - 95.00 + 56.25 + 64.58 + 22.91 + 92.91 2 - 44.37 - 28.75 - 37.18 - 56.25 + 47.44 + 38.35 + 28.69 + 48.29 3 - 36.71 - 25.78 - 15.42 - 25.39 + 40.00 + 21.87 + 18.12 + 25.41 - Average - 45.89 - 40.01 - 23.78 - 50.18 + Mean + 46.08 + 36.84 + 22.66 + 48.04
@@ -3450,10 +3642,10 @@

How many single-color paths go from A to D? Answer with a number in curly br Subway Map 1 Subway Map 2 How many single-color paths go from A to D? Answer with a number in curly br src="./static/images/SubwayConnections/appendix_figs/pixels_512_linewidth_20_path_3_941abb65-fa92-45ac-9ecc-d155646cbbe6.svg" alt="Subway Map 6"> - - 1✓ + 2✗ - 1✓ + 0✗ 2✗ @@ -3519,8 +3710,8 @@

How many single-color paths go from A to D? Answer with a number in curly br 2✗ - 1✓ + 2✗ 3✗ @@ -3578,7 +3769,7 @@

How many single-color paths go from A to D? Answer with a number in curly br

-
Fig. 15: Some VLMs (Gemini-1.5, +
Fig. 14: Some VLMs (Gemini-1.5, Sonnet-3) surprisingly fail in even extremely easy cases (leftmost). As the @@ -3589,7 +3780,121 @@

How many single-color paths go from A to D? Answer with a number in curly br

- +
+
+
+

Overview of All Tasks

+ +
Accuracy of each model over 7 tasks. The mean accuracy over all four models is 58.12% with + Sonnet-3.5 being the best at 74.01% + accuracy. +
+
+
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