- 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.
-
- 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.
-
-
-
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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 1: Examples of 2D line plots used in the task, showing different numbers of intersections.
-
-
-
Prompts
-
-
- We ask each question using two different wordings:
-
-
-
"How many times do the blue and red line plots cross each other?"
-
"How many times do the blue and red lines intersect?"
- The following table shows the performance of the four models on the task of counting line intersections.
-
-
-
-
-
Thickness
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
2
-
45.00
-
70.00
-
64.00
-
80.00
-
-
-
3
-
47.00
-
68.00
-
66.00
-
79.00
-
-
-
4
-
54.00
-
71.00
-
62.00
-
73.00
-
-
-
Average
-
48.67
-
69.67
-
64.00
-
77.33
-
-
-
-
-
-
Qualitative samples
-
-
-
-
How many times do the blue and red lines intersect?
-
-
-
-
-
-
-
-
-
-
-
-
-
-
1✗
-
-
1✗
-
-
2✗
-
-
2✓
-
-
2✓
-
-
1✗
-
-
-
-
-
1✗
-
-
1✗
-
-
1✓
-
-
1✗
-
-
1✗
-
-
1✗
-
-
-
-
-
1✗
-
-
1✗
-
-
2✗
-
-
1✗
-
-
1✗
-
-
1✗
-
-
-
-
-
1✗
-
-
0✓
-
-
2✗
-
-
1✗
-
-
1✗
-
-
2✓
-
-
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
-
- Fig. 2: VLMs cannot reliably count the intersections.
-
-
-
-
-
-
-
-
-
-
Task 2: Two circles
-
-
-
-
-
- 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.
-
-
-
Images
-
- We generate 672 images of two circles on a white canvas. The circles vary in size, distance, and orientation:
-
-
-
Circle diameters: 1/4, 1/5, 1/6, or 1/7 of the canvas size
-
Distances between circle perimeters: -0.15 to 0.5 times the diameter
-
Orientations: 90°, 0°, -45°, and 45° angles with the x-axis
-
Canvas sizes: 384, 769, and 1155 pixels
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 3: Examples of two-circle images used in the task, showing different configurations.
-
-
-
-
Prompts
-
-
- We ask each question using two different wordings:
-
-
-
"Are the two circles touching each other? Answer with Yes/No."
-
"Are the two circles overlapping? Answer with Yes/No."
-
-
-
Groundtruth
-
-
- Answers are based on the distance d between circle perimeters:
-
-
-
d < 0: Overlapping and touching
-
d = 0: Non-overlapping but touching
-
d > 0: Non-overlapping and non-touching
-
-
Random-baseline accuracy: 50%.
-
-
-
-
-
-
Results
-
- The following table shows the performance of the four models on the task of counting line intersections.
-
-
-
-
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
Overlapping
-
71.27
-
93.30
-
88.09
-
88.83
-
-
-
Touching
-
74.10
-
92.26
-
80.95
-
94.49
-
-
-
Average
-
72.69
-
92.78
-
84.52
-
91.66
-
-
-
-
Qualitative samples
-
-
-
-
-
-
-
-
Are the two circles overlapping? Answer with Yes/No.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Yes✓
-
-
Yes✓
-
-
Yes✗
-
-
Yes✗
-
-
No✓
-
-
Yes✗
-
-
-
-
-
No✗
-
-
Yes✓
-
-
Yes✗
-
-
No✓
-
-
No✓
-
-
No✓
-
-
-
-
-
Yes✓
-
-
Yes✓
-
-
Yes✗
-
-
Yes✗
-
-
Yes✗
-
-
No✓
-
-
-
-
-
No✗
-
-
No✗
-
-
No✓
-
-
No✓
-
-
No✓
-
-
No✓
-
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
Task 3: The circled letter
-
-
-
-
-
- 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.
-
-
-
Images
-
- 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.
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 5: Examples of circled letter images used in the task, showing different words and circled
- letters.
-
-
-
-
Prompts
-
-
- We ask each question using two different wordings:
-
-
-
"Which letter is being circled?"
-
"Which character is being highlighted with a red oval?"
-
-
-
Groundtruth
-
-
- Letters need to match predicted letters exactly (case-insensitive).
-
-
-
-
-
-
-
Results
-
- The following table shows the performance of the four models on the task of identifying the circled letter.
-
-
-
-
Word
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
Acknowledgement
-
69.03
-
97.50
-
82.64
-
91.11
-
-
-
Subdermatoglyphic
-
63.60
-
91.05
-
71.45
-
94.49
-
-
-
tHyUiKaRbNqWeOpXcZvM
-
77.92
-
89.90
-
65.94
-
82.08
-
-
-
Average
-
70.18
-
92.81
-
73.34
-
89.22
-
-
-
-
-
Qualitative samples
-
-
-
-
Which letter is being circled?
-
-
-
-
-
-
-
-
-
-
-
-
-
-
o✗
-
-
e✗
-
-
t✗
-
-
o✗
-
-
o✗
-
-
z✗
-
-
-
-
-
w✗
-
-
m✓
-
-
n✓
-
-
p✓
-
-
o✗
-
-
v✓
-
-
-
-
-
o✗
-
-
e✗
-
-
e✗
-
-
y✗
-
-
a✗
-
-
t✗
-
-
-
-
-
l✓
-
-
e✗
-
-
t✗
-
-
h✗
-
-
t✓
-
-
m✗
-
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Task 4: Counting overlapping shapes
-
-
-
-
-
- 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.
-
-
-
Images
-
- 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}.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 7: Examples of Olympic-like logo images used in the task, showing different numbers of shapes,
- sizes,
- and colors.
-
-
-
Prompts
-
-
- We ask each question using two different wordings:
-
-
-
"How many {shapes} are in the image? Answer with only the number in numerical format."
-
"Count the {shapes} in the image. Answer with a number in curly brackets e.g. {3}."
-
-
- Where {shapes} is either "circles" or "pentagons" depending on the image.
-
- The following table shows the performance of the four models on the task of identifying the circled letter.
-
-
-
-
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
Circles
-
42.50
-
20.83
-
31.66
-
44.16
-
-
-
Pentagons
-
19.16
-
9.16
-
11.66
-
75.83
-
-
-
-
-
-
-
Qualitative samples
-
-
-
-
-
How many circles are in the image? Answer with only the number in numerical format.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
5✗
-
6✓
-
5✗
-
10✗
-
10✗
-
5✗
-
-
-
-
5✗
-
5✗
-
5✗
-
5✗
-
5✗
-
5✗
-
-
-
-
5✗
-
5✗
-
5✗
-
10✗
-
10✗
-
5✗
-
-
-
-
5✗
-
6✓
-
6✓
-
10✗
-
9✓
-
7✓
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- Fig. 8: Gemini-1.5
- Pro often predicts "5" circles.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Task 5: Counting the nested squares
-
-
-
-
- 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.
-
-
-
Images
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 9: Examples of nested square images used in the task, showing different numbers of squares.
-
-
-
-
Prompts
-
-
- We ask each question using the following wording:
-
-
-
"Count the total number of squares in the image."
-
-
- Where {shapes} is either "circles" or "pentagons" depending on the image.
-
- The following table shows the performance of the four models on the task of counting nested squares.
-
-
-
-
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
Squares
-
48.33
-
80.00
-
55.00
-
87.50
-
-
-
-
-
-
-
-
Qualitative samples
-
-
-
-
Count total number of squares in the image.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
5✗
-
5✗
-
5✗
-
5✗
-
6✗
-
6✗
-
-
-
-
5✗
-
5✗
-
5✗
-
5✗
-
5✓
-
5✓
-
-
-
-
5✗
-
5✗
-
5✗
-
5✗
-
4✗
-
4✗
-
-
-
-
4✓
-
4✓
-
4✓
-
4✓
-
4✗
-
4✗
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- Fig. 10: Only Sonnet-3.5 can count
- the
- squares in a majority of the images.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Task 6: Counting the rows and columns of a grid
-
-
-
-
- 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.
-
-
-
Images
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Fig. 9: Examples of grid images used in the task, showing text-filled and empty grids with various
- dimensions.
-
-
-
Prompts
-
-
- We ask each question using two different wordings:
-
-
-
"Count the number of rows and columns and answer with numbers in curly brackets. For example, rows={5}
- columns={6}"
-
"How many rows and columns are in the table? Answer with only the numbers in a pair (row, column),
- e.g.,
- (5,6)"
-
-
-
Groundtruth
-
-
- Answers include both the number of rows and columns. An answer is correct when both column and row counts
- are
- correctly predicted.
-
-
-
-
Results
-
- The following table shows the performance of the four models on the task of counting rows and columns in
- grids.
-
-
-
-
Grid type
-
-
- GPT-4o
-
-
-
-
- Gemini-1.5 Pro
-
-
-
-
- Sonnet-3
-
-
-
-
- Sonnet-3.5
-
-
-
-
-
Blank
-
26.13
-
25.75
-
25.00
-
59.84
-
-
-
Text
-
53.03
-
45.83
-
47.34
-
88.68
-
-
-
Average
-
39.58
-
35.79
-
36.17
-
74.26
-
-
-
-
-
-
-
Qualitative samples
-
-
-
-
Count the number of rows and columns and answer with numbers in curly brackets. For example, rows={5}
- columns={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✗
-
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- Fig. 12: Examples from the benchmark show that models consistently fail at counting rows and
- columns of
- blank grids.
-
-
-
-
-
-
-
-
-
-
How many rows and columns are in the table? Answer with only the numbers in a pair (row, column),
- e.g., (5,6).
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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✓
-
-
-
-
-
- GPT-4o
-
-
- Gemini-1.5
- Pro
-
-
- Sonnet-3
-
-
- Sonnet-3.5
-
-
-
- Fig. 13: When text is included in the cells of the grid, the performance of all VLMs improves,
- especially
- Sonnet-3.5.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Task 7: Following single-colored paths
-
-
-
-
-
- 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.
-
-
-
Images
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- 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:
-
-
-
"How many single-colored paths go from A to C? Answer with a number in curly brackets, e.g., {3}"
-
-
"Count the one-colored routes that go from A to C. Answer with a number in curly brackets, e.g.,
- {3}."
-
- 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
-
-
-
-
-
1
-
67.50
-
85.41
-
23.75
-
95.00
-
-
-
2
-
44.37
-
28.75
-
37.18
-
56.25
-
-
-
3
-
36.71
-
25.78
-
15.42
-
25.39
-
-
-
Average
-
45.89
-
40.01
-
23.78
-
50.18
-
-
-
-
-
Qualitative samples
-
-
-
-
How many single-color paths go from A to D? Answer with a number in curly brackets e.g. {3}
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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
-
-
- Gemini-1.5
- Pro
-
-
- 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.
-
-
-
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.
-
-
-
-
@@ -1506,8 +1616,12 @@
Prompts
We ask each question using two different wordings:
-
"How many times do the blue and red line plots cross each other?"
-
"How many times do the blue and red lines intersect?"
+
"How many times do the blue and red lines touch each other? Answer with a number in curly brackets,
+ e.g.,
+ {5}."
+
"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
-
Thickness
+
Line width
GPT-4o
@@ -1550,34 +1661,28 @@
Results
-
2
+
0.005 × C
45.00
-
70.00
-
64.00
-
80.00
-
-
-
3
-
47.00
-
68.00
-
66.00
-
79.00
+
67.55
+
45.22
+
75.83
-
4
-
54.00
-
71.00
-
62.00
-
73.00
+
0.01 × C
+
38.22
+
66.33
+
41.61
+
74.88
-
Average
-
48.67
-
69.67
-
64.00
-
77.33
+
Mean
+
41.61
+
66.94
+
43.41
+
75.36
+
@@ -1590,24 +1695,24 @@
How many times do the blue and red lines intersect?
-
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
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-How many circles are in the image? Answer with only the number in numerical
-
-
-
+
+
-
+ alt="Circle 6">
+
5✗
-
6✓
+ class="check">✓
+
5✗
-
10✗
-
10✗
+ class="cross">✗
+
+
7✗
+
+
12✗
+
+
11✗
+
5✗
+ class="cross">✗
+
5✗
+ class="check">✓
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
+
3✗
+
5✗
-
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
10✗
+ class="cross">✗
+
10✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
-
5✗
+
4✗
+
6✓
+ class="check">✓
+
6✓
+ class="check">✓
+
10✗
+ class="cross">✗
+
9✓
+ class="check">✓
+
7✓
+ class="check">✓
+
@@ -2685,7 +2819,11 @@
Prompts
We ask each question using the following wording:
-
"Count the 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}."
+
+
"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}.
+
+
+
3✗
+
5✗
+ class="cross">✗
+
+
3✓
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
5✗
+ class="cross">✗
+
6✗
+ class="cross">✗
+
6✗
+ class="cross">✗
+
+
2✓
+
+
3✓
+
+
2✗
+
+
3✓
+
5✗
-
5✗
-
5✗
-
5✗
+ class="cross">✗
+
5✓
+ class="cross">✗
+
5✓
+ class="check">✓
+
+
4✗
+
+
2✓
+
+
4✗
+
+
2✗
+
+
4✗
+
5✗
-
5✗
-
5✗
-
5✗
+ class="cross">✗
+
4✗
+ class="check">✓
+
4✗
+ class="cross">✗
+
+
5✓
+
+
2✓
+
+
3✓
+
+
3✓
+
+
3✓
+
4✓
-
4✓
-
4✓
-
4✓
+ class="check">✓
+
4✗
+ class="check">✓
+
4✗
+ class="cross">✗
+
+
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
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
+
+
+
+
+
Model
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Mean
+
+
+
+
+
+
41.61
+
72.67
+
70.18
+
55.83
+
42.50
+
17.50
+
39.58
+
46.08
+
48.25
+
+
+
+
66.94
+
92.78
+
92.81
+
80.00
+
87.08
+
19.37
+
39.39
+
36.84
+
64.40
+
+
+
+
43.41
+
84.52
+
73.34
+
65.00
+
31.66
+
9.79
+
36.17
+
22.66
+
45.81
+
+
+
+
75.36
+
91.66
+
89.22
+
92.08
+
44.16
+
77.29
+
74.26
+
48.04
+
74.01
+
+
+
Mean
+
56.84
+
85.41
+
81.39
+
73.29
+
51.35
+
30.99
+
47.35
+
38.41
+
58.12
+
+
+
+
+ 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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