VLM’s Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models
Authors: Nam Hyeon-Woo
Moon Ye-Bin
Wonseok Choi
Lee Hyun
Tae-Hyun Oh
Venue: Accepted by TMLR ()

Abstract: Vision language models (VLMs) have shown promising reasoning capabilities across various benchmarks; however, our understanding of their visual perception remains limited. In this work, we propose an eye examination process to investigate how a VLM perceives images, focusing on key aspects of visual recognition, ranging from basic color and shape to semantic understanding. We introduce a dataset, LENS, to guide VLMs to follow the examination and check its readiness. Once the model is ready, we conduct the examination. We quantify and visualize VLMs' sensitivities to color and shape, and semantic matching. Our findings reveal that VLMs have varying sensitivity to different colors while consistently showing insensitivity to green across different VLMs. Also, we found different shape sensitivity and semantic recognition depending on LLM's capacity despite using the same fixed visual encoder. Our analyses and findings have the potential to inspire the design of VLMs and the pre-processing of visual input to VLMs for improving application performance.

Published in Transactions on Machine Learning Research (03/2025)
VLM's Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models
Nam Hyeon-Woo
hyeonw.nammppstech..cco
Deportment of Electrical Engineering
POSTECH
Moon Ye-Bin
ybmmoon&postechh
Department Electrical Engineering POSTECH
Wonseok Choi
wonseok.c@postedee
Grad. School of Al POSTECH
Lee Hyun
hayun www.:100...ee@mmsssnnn.o.o com
Department of Electrical Engineering, POSTECH Samsung Al Center, Samsung Electronics
Tac-Hyun Oh
thoh, kaist. ac. for @ggmaill com
School of Computing, KAIST Department of Electrical Engineering & Grad. School of AI, POSTECH
Reviewed OpenReview: /ttps://openreeeee net/ forum? id= CgWWVb2LL
Abstract
Vision language models (VLMs) have shown promising reasoning capabilities across various benchmarks; however, understanding of their visual perception remains limited. In this work, propose eye examination process to investigate how VLM perceives images, focusing on key aspects of visual recognition, ranging from basic color and shape to semantic understanding We introduce a dataset, LENS, lo guide VLMs to follow the examination and check its readiness. Once the model ready, we conduct the examination. We quantify and visualize VLMs' scnsitivitics to color and shape, and semantic matching. Our findings reveal that VLMs have varying sensitivity different colors while consistently showing insensitivity to green across different VLMss. Also, found different. shape sensitivity and semantic recognition depending LLM's capacity despite using the same fixed visual encoder. Our analyses and findings have the potential to inspire the design of VLMs and the pre-processing of visual input to VLMs for improving application performance.
1 Introduction
Vision language models (VLMs) (Liu et al.] 2023b Dai et al. 2023 OpenAl 2023 Chen al. 2023
are composed of visual encoder to process visual information with large language model (LLM) for comprchension, akin to how the human visual system operates with the eyes and brain. While VLMs have shown promising performance on various tasks (Marino et. al. 2019 Mishra et al. 2019 Sidorov el al. 2020
Krishna et al. 2016), our understanding of how these models perceive visual information remains limited.
Prior works (( (Choe al. 2022 Zhou et al. 2016 Akata al. 2023 Prystawski et al. 2023 Zhu
2023
Allen- -hlen-Zhu & Li 2023) have tried understand the behavior or decision of neural networks, which would
help to achieve responsible AT including explainability and safety. As the need for model understanding is

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Published in Transactions on Machine Learning Research (03/2025)
Eye Examination Process
E P
1) Instruction Finetune VLM with LENS train set
2) Readiness Check
3) Examination
Evaluate VLM on LENS test set
Inspect VLM exploiting LENS format
T TOF
Eye Examination Results
Result Sheet
Result Sheet
Name: LLavA 19B
Date : May, 2024
Name: LLavA 7B
Date May, 24rd 2014
0 Color Distinction
Shape Distinction
O Color Distinction
G Shape Distinction
Red: Good
Nor
Red: Good
Bad
Green: Bad
Semantic Distinction
Green: Bad
Semantic Distinction
Blue: Norwwl
Blue: Norvwa
Good
Notval
Eye examination. The process of eye examination contains three steps: instruction, readiness check, and examination. a VLM has acquainted instructions and is ready, the model conducts examinations of color, shape, and semantic comparisons to assess its visual competency.
Figure 1:
becoming increasingly important alongside significant advances in VLMs, raise fundamental question: How do VLMs see and recognize?
VLMs can understand questions and answer them in human-undorstandable language; therefore, we propose an eye examination process for VL.Ss by directly asking them questions to assess their visual recognition capabilitics. However, directly asking VLMs unfamiliar questions without providing prior information about the examination does not yield meaningful results. Inspired by human vision lesting, where participants design similar protocol: (1)
receive instructions about how to conduct the test before the examination, Instruction - let a VLM know how the eye examination will proceed, (2) Readiness check - -veiff if the VLM is ready, and then (3) Examination conduct the eye examination with questionnaires (refer to Fig. D For (1) instruction and (2) readiness check, we introduce a synthetic dataset named LENS (Learning ElemeNt for visual Sensory), categorized into basic visual elements such as color, shape, and semantics.
After the model passes the readiness check, we assess its recognition ability by comparing the reference and larget objects in the step (3) examination. For example, in the color test, we ask if the VLM dist ingting subtle color differences such as standard red pinkish-red. By conducting this comparative analysis, we can assess the scnsitivity of VLMs to specific visual clements. For systematic analysis, we define the metrics of sensitivity: Sensitivity Area of Color (SAC) and Sensitivity Area of Shape (SAS). This reveals that the sensitivity levels are different for the reference colors and reference shapes. For semantic elements, we devise a pitch-wise comparative analysis to reveal the semantic sensitivity of the VLM.
We perform the eye examination on LLaVA (Liu al., 2023a) and InstreutBLIP (Dai al. 2023): we also employ advanced VLMs (Chen et al. 2024c Wang al. 2024 Marafioti et al. 2024) that do not require additional fine-tuning. The examination results reveal that the models have similar trends in visual competency, such as being less sensitive to the green color and having a similar tendency to shape. We include comparative analyses between human visual cognition and VLMs and investigate the effect according to the different scales of the LLM module. Our examination helps to undorstand the models' resolution in dist inguishiin similar colors and shapes. As a potential application, in chart image understanding, we can apply simple preprocess to visual input, considering the model's sensitivity, so that we can improve the ability of VLMs to recognize chart images.
2 LENS Dataset for Instruction and Readiness Check
Understanding primitive competency at different levels is fundamental to vision perception as it the basic
building block of human cognition (Vo Glasersfeld 1989
Marr 2010
Lowe 2012). To understand the visual
2

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Published in Transactions on Machine Learning Research (03/2025)
Color
Shape
Semantics
SAMPLE
SAMPLE2
SAMPLE
SAMPLE
SAPPLE
SAMPLEZ
Y/N: the samples have the same color? Answer yes
Y/N: Are the two samples identical in shape? Answer or
Y/N: the samples have the same senantics? with yes
which one is extracted alternate inage?
Which two patches are out their original positions?
1/2: Each sample has two colored boxes, which sanple has the sane colored boxes? Answer sample 1, sample 2, No answer.
1/2: Each sample has two shapes, offer answer which sample has the same shape. Answer with sanple 1, sample 2, or answer.
Each sample has two images, give clear answer to which has the sane semantic. Answer sample 1, sample 2, No answer.
Given the picture divided into 16 patches with sone missing, identify where the patch the right fits.
Figure 2: LENS dataset. We visualize the samples in LENS, which is designed to instruct VLM and check its readiness. LENS contains three categories: color, shape, and semantics. Note that questions for each sample are randomly sampled from a pre-defined question set, and the prompts for patches contain two options along with a no answer option. More details and data statistics can be found in the Appendix.
competency of VLMs, we propose an eye examination process involving three main steps: 1) instruction, 2) readiness check, and 3) examination. For steps 1 and 2, introduce dataset called LENS (Learning ElemeNt, for visual Sensory), which has three primitive element categories: color, shape, and semantics. To give an instruction to a model about how to perform the examination, we finetune VLMs using LoRA (Hu et al. 2022) on the training set of LENS. Then, the Lest set of LENS is utilized to check the model's 's understanding of the instructions. Each sample of LENS data consists an image, a question with answer options, and its ground truth answer (sce Fig. 2 The questions are randomly sampled from a set associated with each category. If the model performs well on the LENS test set, we proceed examine the model. The LENS format is also utilized in the examination stage. The examination step is described in Secs. 3.1
2.1
Color LENS
The color element is designed address specific queries categorized into cither yes/no Sample or Sample 2, as shown in the first column of Fig. 2 The yes/no question type involves pairs of colors, prompting assessment of whether the two colors are identical. The model should respond with or no, called format. The Sample 1 or Sample 2 question type includes two sets, each containing two colors. The model should choose the correct sample or respond with no answer if both samples have different colors. The challenge lies in determining which sample pair accurately matches terms color. Note that we provide two separate color sets for training and test data, respectively.
We finetune LLaV1-11.5 [Liu al. 2023a) and InstructBLIP DDii al. 2023 with LoRA (Hu et al. 2022) our color LENS training data. In Table Ia the performance of both LLaVA and InstructBLIP becomes higher after finetuning and reaches acceptable levels on our color test set, regardless of their model size. This higher performance indicates that the models understand the instructions and are ready for the examination regarding color. The instructions and readiness check steps are performed in the same way for shape and semantic elements as for color. To measure visual competency on particular element, we use a model separately finetuned on the training for that element. We will examine visual perception in terms of color in Sec. 3.1
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Table I: Readiness check. We evaluate LLaVA and InstructBLIP on the LENS test set after fnn-ttning them on the LENS training set. The accuracy improvement after fine-tuning implics that LENS enhances the model's ability to compare two samples, indicating the model is ready to take the eye examination. Y/N stands for yes or no, 1/2 for sample 1 sample 2, and S and P for the semantic and patch groups, ft for fine-tuning, respectively.
(a) Color
(b) Shape
(c) Semantics
Model
Yes or No
1 or 2
Model
Yes or No
1 or
Model
S-Y/N
S-1/2
P-Cross
P-Selt
P-Mask
Random
50.00
33.33
Random
50.00
33.33
Random
50.00
33.33
33.33
33.33
33.33
LLaVA-7B
41.20
LL-VA-7B
58.27
39.05
LLaVA-TB
67.10
22:69
36.73
32.47
46.67
Color (8)
89.79
98.59
+ Shappe (It)
78.15
99.85
Sommnties (8)
81.70
95.00
41.47
47.93
50.00
LLaVA-13B
96.48
32.30
LLaVA-13B
61.07
48.45
LLaVA-13BB
70.70
25.19
18.60
35.00
22.53
Coloz (8)
99.65
+ Shape (ft)
80.60
99.70
+ Semantics (a)
91.42
98.60
73.33
511.7
Instrwc.BLIP-38
89.79
47. 53
InstructBLIP
61.43
50.00
IustructBLI--33 3B
09.30
50,00
41.07
28.40
33:27
+ Color (ft)
100.0
72.53
+ Shape (ft)
100.0
99.55
+ Semanties (ft)
84.30
83.65
41.20
48.07
50.07
Instruc:BL11777
50,00
50.00
IustructBLIP-TT
52.44
50.00
lustructBLIP-TB
19.70
46.31
20.80
33.27
21.80
Color (ft)
100.0
100.0
+ Shape (ft)
100.0
99.94
Somantics (it)
84.00
88.08
40.47
48.33
50.27
2.2
Shape LENS
The format for the shape element is the same as the color element but with color boxes replaced by shape images. We create these shape images using Bezier curves. The process begins with generating random points that are smoothly connected. The hyperparameters include the number of points, the radius around points, and the smoothness of the curve. We ensure that these hyperparameters do not overlap between the training and test sets. The second column of Fig. 2 shows the shape samples, with the readiness check results in Table 1b The shape element examination is detailed in Sec. 3.3
2.3 Semantic LENS
The semantic element has two groups, semantic and patch. The last column Fig. P ssoww the samples for these groups, where the left is the semantic group and the right the patch group. Semantic groups follow the same format as color or shape datasets but use images from ImageNet (Deng et al. 2009). The patch group has 3 types of images: self-swap, cross-swap, and masking, as shown in Fig. In all cases, we divide the images sampled from ImageNet into 4 x 4 patches. The self-swap requires finding the positions of swapped patches, the cross-swap requires identifying a patch from a different image. The masking requires locating the correct position for one of the missing patches. All questions in the patch group offer three options, including the option of no answer. We check the readiness on the semantic element in Table Ic The examination of the semantic element is detailed in Soc. 3.5
3
Eye Examination for VLMs
During the examination, we evaluate the discrinination abilitics of VLMs by asking whether two samples are identical in the context of multiple levels of vision primitives. In this paper, we conduct eye examination
VA-v1.5 (Liu et al. 2023a) and InstructBLIP DDai et al., 2023 We also provide the examination
results of recent advanced VLMs without additional fine-tuning, such as ILte5 (Cen et. al. 2024c).
Qwen2-VL VL (Wagg et al. 2024). and SmolVLM (Marafioti et al. 2024).
3.1 Examination: Color
Color is an important feature for distinguishing objects and is fundamental to many aspects of computer vision (Gevers et al., 2012). By distinguishing subtle color differences, we can perceive detailed information about an object, such as its curvature. We explore how VLMs perceive and process a subtle difference in color information and understand VLMs' color resolution.
4

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LLaVA
InstructBLIP
Red
Green
Blue
Red
Green
Blue
Color Wheel
3B
138
78
Figure 3: Visualization Color sensitivity. We measure the sensitivity of VLMs in differentiating bet ween reference and target colors. Visualization of color sensitivity represented by f(ret Starget), where Cref is one of red, green, and blue, and Ctarget. is selected from the color wheel. The result that green has the largest area of the wheel indicates distinguishing green is more challenging compared to red or blue.
We first look into how well VLMs can distinguish similar colors, utilizing the yes or no format. To measure color sensitivity, especially for the representative colors RGB, we proceed as follows:
1. Select reference color Gref from red, green, and blue, and paint sample 1 with the selected Cref-
2. Choose a larget color Otarget from the HSV color wheel in Fig. 3 and use the chosen Orarget sample 2. The radius and angle on the wheel are divided equally into 100 and 500, respectively. This results in a total of 50k possible colors for Otarget
3. Ask VLMs whether the reference and target colors are the same. Then, record the token probability of "yes", denoted as pe"yes"" "losf Ctarget).
When the input has Cref and Gtarget we extract token logits corresponding to "yes" and "no," and normalize the "yes" logit by these logits, denoted as the score function ((reef Ctarget) {Cref: Ctarget) 10. In summary, color sensitivity is visualized by p("yes"((crr Croff Otarget) [[Ceff Ctarget). To quantify the visualized color sensitivity, define it follows.
Definition 1. Sensitivity Area of Color (SAC). Let eref be the reference color, Ctarget the target color, and (Cret, Gaarget) {Crets Grarget) + [0, 1] score function that measures the similarity between colors. The function assigns a high value when the model recognizes that colors are similar.
Sensitivity Area of Color = f f(cref: Orarget ddcaarget.
(1)
However, since calculating the integral is not feasible, we use numerical integration as Σi=l f'(ree- Ctarget )dA, where dA, is the differential area. As shown Fig. 3. we perform the caleulation in polar coordinates, so dA, rdødr where r and & are the radius and angle, respectively. low SAC value indicates that the model capable of sensitively distinguishing the reference color. Conversely, high SAC value stands for the model's insensitivity to the reference color.
Result. In Fig. both LLaVA and InstructBLIP show high color sensitivity to red and low to green color. The visualization result in Fig. shows that, although each reference color has high probability in the neighborhood of cach color, red is the most discriminating (sonsitive), while green is the least discriminating (insensitive). The results are opposite to humans that are sensitive to green (Pasmanter & Munakomi] 2019 Robinson & Schmidt, 1984 Serway). Table 2 lists the SAC values that are consistent with the visualization. When comparing two models with the same size (7B), LLaVA is more sensitive to color distinctions. Ad- vanced models without additional fine funituning, specifically InterVL2.. Qwen2-VI, and SmoIVLM, generally align with the trend. The additional discussion be found in the appendix.

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Human-view
LLaVA-view
InstructBLIP-view
Table 2: Sensitivity Area of Color (SAC). SAC quantifies the area the wheel as defined in Defini- tion We denote fine-tuning as ft. Consistent with the visualizations Fig. the value for the green is the highest.
Model
Size
Red
Green
Blue
7B
0.0064
0.0479
0.0156
LLaVA (ft)
13B
0.0036
0.0202
0.0105
3B
0.0361
0.0533
0.0374
InstructBLIP (ft)
7B
0.0224
0.0793
0.0336
2B
0.1144
0.3953
0.1948
Figure 4: Color correction. The first column is the original images that humans see. The second and third columns show the transformed images based on the color similarity patterns perceived by LLa VA and InstructBLIP, respectively. VLMs see the world greener.
InternVL.2.5
4B
0.0772
0.1827
0.1026
8B
0.0740
0.2023
0.1121
SmolVLM
Instruct
0.2487
0.8676
0.2652
2B
1.0727
0.9949
1.0868
Qwen2 VL
7B
0.1108
0.3920
0.1592
Interostingly, while humans are sensitive to green Pasmanter & Munakomi 2019 Robinson & Schmidt, 1984 Serway), VLMs show the opposite result. Humans are more sensitive lo medium wavelengths (often perceived as green) compared to long (xed-sensitive) and short (blue-sensitive) wavelengths, resulting in heightened sensitivity to green. Inspired by this biological fact, we question the underlying factors leading varying color sensitivities in VLMs. Specifically, we seek to understand the origins of these sensitivity variations in VLMs. Therefore, we investigate this further in the following section.
3.2 Why do VLMs have different color sensitivities?
What component does affect the color sensitivity of VLMs? We hypothesize that the capability of the visual encoder to perceive color significantly influences the decision-making process of LLMs, even more than LLMs themselves. To illustrate this, consider thought experiment on the color sensitivity that converts the RGB value the text and then ask for the probability from L.Ms without visual encoder. For instance, can prompt like *Are (255, 0, 0) and (0, 255, 0) the same color? (Patel & Pavlick 2022) Since LLMs do not need to directly discriminate the colors themsclves, we would expect the sensitivitics to distinctive colors to similar, leading us to question the role of the visual feature.
We measure cosine similarity between colors as follows: is a visual encoder. We fixed the reference color red, green, blue and varied the target color within the 24-bit bit RGB color space. Considering the vast size of the color space, 256 Rs 16.8M, we reduce the color space to 323 = 32, 768. We apply min-max normalization to ensure values within the range [0, 1].
sirn(Craf, Crarget) = o(cer))-(((rrre))
[(Ceet) (Cll where v(-)
We evaluate CLIP ViT-L/14 336px (Radford et al. 2021) and ViT-g/14 (Fang al. 2023) a visual encoder of LLav1.11.5 LLu et al. 2023a) and InstructBLIP (Dai et al. 2023). respectively. When computing color similarity between hidden features for RGB and other colors, notice that the pattern for green is opposite to that of red and blue. Details of the similarity pattern can be found in Fig. [1] in the Appendix. We use these patterns to convert the original image into an image perceived by VLMs; we refer to this process as color correction. The transformation of the original RGB value is: La (255, 0) simm(Rea Iew) + (0, 255, 0)- sim (CCrCCere Ix,) + (0, 0, 255). sim(CBlue: Iz,),, where L and In,, stand for color-corrected and original image pixels.
Result. In Fig. 4 the results show how LLaVA and InstructBLIP perceive the world compared the original images as perceived by humans. Surprisingly, the color-corrected images of both models appear

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Published in Transactions on Machine Learning Research (03/2025)
Ref.
Ref.
Ref.
High
High
High
(a) Eccentricity
(b) Polygon
(c) Size
7B
7B
13B
-30
13B
Score
Score
Score
0.5
0.5
0.5
0.40
-11
-15
-49
0.32
138
0
0
0.3
0.6
0.9
6
12
18
24
30
0
30
60
90
120
150
180
Eccentricity
Polygon
Size
(d) Eccentricity sensitivity (+)
(e) Polygon sensitivity (+)
(f) Size sensitivity (-++
Figure 5: Shape sensitivity. We measure the sensitivity of VLMs between circle and target shapes by varying (a) the eccentricity of a circle, (b) the number of vertices in a regular polygon, or (c) the size of a circle. The model is more sensitive the score changes at (d) lower eccentricity, (e) a higher number of
vertices, and (f) a narrower range size. The results shows that a larger VLM is more sensitive than a smaller one.
greener than their original ones. The result implies that the models perceive the world greener, enabling them distinguish red and blue more than green. In summary, examine the color cognitive ability. Our findings indicate that (1) both models are less sensitive to green, (2) this property is derived from the visual encoder, and (3) this differs from humans who are sensitive to green.
3.3 Examination: Shape
Fundamental features such as edges, corners, and blobs are extensively employed in feature descriptions Krig & Krig (2016) Mikolajezyk et al. 2003 Mukherjee et al. (2015). We investigate how VLMs process shape information. We examine the shape sensitivity similar to the color sensitivity in Sec. 3.1 Given the reference shape of circle, the target shape is set by adjusting the circle's eccentricity, size, or the number of regular polygon vertices. As the eccentricity increases (see Fig. 5a), the number of vertices decreases (see Fig. 5b)
the size of the circle increases and decreases (sce Fig. 5e], the target shape deviates from reference circle, denoted Ref. in each figure. We measure the probability of shape sensitivity follows.
The reference shape Stef is a circle, which is sample 1.
Choose a target shape Starget. by varying eccentricity or the number of vertices. Eccentricity ranges from 0 to 0.9, divided into 900 segments. The number of vertices ranges from to 30. The size has 200 levels, where 100 are smaller and the remaining 100 are larger than the reference shape. The chosen Starget is depicted in sample 2.
Ask VLMs if the given shapes are the same, then record the token probabilities of "yes".
Based on the token probability of the shape sensitivity, we define the sensitivity area of a shape.
Definition 2. Sensitivity Area of Shape (SAS). Let 8tef be the reference shape, and Starget the target shape. We define score function f( Starget) :
{Srefs Starget] + [0, 1] that measures the similarity between shapes, assigning a high value when a model recognizes that shapes as similar.
Sensitivity Area of Shape = / f(Sret Starget ddstagget
=
(2)
For the feasible computation, we use numerical integration, where dstarget is 1/1000 for occentricity, 1 for polygon, and for size. Note that low SAS value indicates that the model can sensitively distinguish between the reference circle and the target shape.

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Table 3: Sensitivity Area of Shape (SAS). SAS quantifies the shape sensitivity as defined in Definition 2. We denote fine-tumin tine-tuning ft. The result consistent with the plot shown in Fig. 5
7B
8com
8rom
Model
Model Size
Eccentricity
Polygon
Size
09
0.8
08
12
18
24
Ecoontiiity
Pobgton
7B
0.4059
17.047
145.6
LLaVA (R)
(g) Real
(h) Integer
13B
0.33417
14.986
124.6
ILSP (ft)
3B
0.2084
10.73
80.4
Figure 6: Shape sensitivity of LLMs. The
7B
0.1662
9.74
69.6
text prompts for two-number comparisons are given to LLMs. We extract and plot the proba- bility score of the "yes" token for cach prompt. (a) compares real numbers corresponding to ec- centricity sensitivity. (b) compares integers for polygon sensitivity.
0.6915
17.115
193.7
InterVL
0.4000
10.126
0.3739
10.997
70.5
Qwen2 VI.
2B
0.2160
7.269
56.5
7B
0:2727
6.393
49.3
Base
0,8544
25.785
191.7
SmoIVLM
Instruct
0.4192
8.174
42.9
Result. We visualize the score function of LLaVA in Fig. 50 showing that a larger model more sensitive than smaller one. We mark the point when the first 0.5 score is achieved. In eccentricity sensitivity, the half-score points for LLaVA-7B and 13B are at 0.40 and 0.32, respectively. In polygon sensitivity, the half-score points are at about 11 and 15, respectively. In size sensitivity, the points are 30/179 and 49/177, respectively. InstructBLIP shows a similar trend to LLaVA, i.c., the larger the model, the more sensitive, which can be found Fig. 12 in the Appendix. As shown in Table 3 the SAS values also align with the graph results. Similarly, for InternVL2.5, the larger models generally are more sensitive compared to the smaller ones. Qw also shows a similar overall tendency. For SmolVLM, the Instruct model shows higher sensitivity than the base model, which we attribute to its enhanced ability to follow the given instructions. This leads us to the question: why does shape sensitivity vary with model size?
3.4 Why do VLMs have different shape sensitivities?
What component affects the shape sensitivity? Since we the same visual encoder regardless of model size, the dilference likely stems from the capacity of the LLMs. To investigate this, design experiment to examine decision-making in LLMs by changing the text prompt and observing the token probability of shape sensitivity. The occentricity prompt is "Is {ri} greater than or equal to {r2}? where r1 = 0 and r2 as real number between and 0.9. The polygon prompt is "Is {nr} greater than or equal to {n2)? where n2 and n, as an integer between and 30. We append " "Answer with yes or no." to each text prompt and record the score of "yes" and "no" tokens.
Result.
The scores for each text prompt are plotted in Fig. We observe similar tronds between Fig. 5g
5h and Fig. 5d 5e The large LLM drops below 0.5 faster than the small LLM in Fig, 5d and Fig. 5g and vice versa in Fig. 5e and Fig, 5h We also notice that the smaller LLM less accurate
comparing two numbers than the larger LLM. For example, the correct score should be below 0.5 in the first text prompt because 0 is less than other numbers in the range [0, 0.9], but 7B shows higher score than 0.5, and it is also applied to the second text prompt. In summary, we investigate the shape recognition ability of VLMs follows: (1) larger VLMs are more sensitive to shape, (2) the ppeision-making of LLM influences the shape sensitivity, and (3) the model size of LLMs inffuences the numerical comparison ability, which is connected to the shape sensitivity.
3.5 Examination: Semantics
Semantics represents a foundational component of vision recognition. Humans can classify objects into their semantic classes, regardless of variations in color or shape. This ability extends perceiving partially obscured objects; can infer the form of the hidden parts if recognize the category. In this regard,
8

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Refersnce tng. SAMPLE
Cropped SiMeEE2
Same Class
Same Class
Different Class
Different Class
Source Ing. Sample
(Usee) sarplee (Aastatant)
78
(Relative)
Probubiitsss. 8.99
(a) How measure semantic score map
(b) Semantic score map
Figure 7: Patch analysis. (a) We provide the reference image and the target patch to LLaVA, repeating the process by sliding and then extracting the probability of the "yes" token. (b) We fixate the reference image from (a), vary the target patch, and visualize the score map. When samples and 2 belong to the same class, the scores are higher on the object itself. Notably, the smaller model tends assign higher scores to the background areas.
7B
13B
CLIP
Figure 8: Patch analysis result with visual encoder. We compute the similarity between the reference image and patches. For the CLIP encoder, we adopt cosine similarity score.
the role of semantics is critical in visual perception. Thus, investigate how well VLMs process semantic- related information. The improvement of performance on the semantics in Table Tcl is noticeable in a larger model. These results raise the question of why larger models have better performance on semantics.
3.6 Why do VLMs have different accuracies?
Given the same visual encoder, why do the larger VLMs have higher accuracies on the semantic dataset? We hypothesize that the performance difference comes from LLM because it uses the same visual encoder. To validate this hypothesis, we visualize how LLMs make decisions. Inspired by the weakly supervised object localization method CChoe et al. 2022), we assign probability to patches (see Fig. 7a as follows.
Given two images, one is used as the reference image, which is sample 1. The other is the source of target.
2. Crop the target source image into patches according to the patch size and stride. The cropped patch is used for sample 2.
3. Ask VLMs if the reference and target patches share the same semantics and record the probability of "yes" tokens as the score.
Figure 7b shows the score map for target images according the model size and patch size.
Result. Compared the results for the same and different classes in Fig. 7b the score maps for the same
class have high scores in object-present regions, contrasting with the noisy and structure-lacking maps for different classes. The larger model assigns lower score to the background compared to the smaller model.

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Figure 8 shows the result of VLMs and the corresponding visual encoder. We observe that distinction and discrimination become sharper after passing the LLM. We think that LLMs help to better understand context and complement the visual encoder. It implies that larger LLMs achieve more accurate semantic recognition as reflected in Table Ic. Also, the kernel size affects the reliability of the captured objects and the noisiness of the score map.
Inferring whether a reference image and a patch share the same semantics requires high reasoning capabilities. Prior works (Brown et al. 2020 Zoph et al., 2022) have demonstrated that larger LLMs exhibit better reasoning abilities. Accordingly, when the vision encoder remains consistent, larger LLMs are likely to perform better in understanding and interpreting semantic information.
4 Potential Application
Our findings suggest that VLMs can improve image recognition by applying simple pre-processing to input images. For example, in Fig. g the chart reasoning results from GPT-4 (OpenA] 2023) vary according to graph colors or symbol shapes. Regarding the color, GPT-4 struggles to distinguish between similar shades of color, making it difficult for VLMs match colors with their corresponding numerical values. As revealed by the color sensitivity check, this result. is due to the model's lack of color competency rather than its reasoning ability. By adjusting the colors, GPT-4 provides accurate responses. Regarding the shape, GPT-4 is confused between triangle and reclangle symbols, indicaling a lack of shape competency that may result in incorrect recognition outcomes. When we change the rectangle to a circle, GPT-4 provides the correct reasoning output.
Our eye examination framework can also guide the selection of necessary components. For instance, tasks like anomaly detection often demand advanced perception capabilities related to shape, color, and other effectively
visual features. By designing the eye examination to the specific roquirements of the task, identify and prioritize models that contribule to demonstrating performance in examinations. We hope that our VLM's eye examination process will not only assist in selecting models and manipulating images but also facilitate enhancements in model performance by deepening our understanding of underlying factors.
5
Related Work
LLMs
OpenAl 2023
Touvron et al. 2023a b Zhang et al.] 2022b Chowdhery et al. 2023 Roberts et al.
Chiang et al. 2023) have shown a remarkable ability to process text and language information and generate rational responses. Building on the success of LLMs, VLMs (Dai al. 2023 Liu et al. 2023b a Gao et al. 2023b Zhang et al., 2024) have emerged. These models, which combine visual encoders with
2020
LLM inference capabilities, have made significant progress. Unlike LLMs, VLMs are capable of processing visual information such as images. VLM needs to interpret the information in an image based on given task or prompt; instruction-tune VLMs, such as LLaVA (Liu et al.) 2023b) and InstructBLIP (Dai et al. 2023), have demonstrated gencralizability in addressing multiple tasks. To improve our understanding of VLMs, we focus primarily on understanding how VLMs recognize and interpret visual information.
Both humans and neural networks exhibit similarities in terms of information processing (Alper et al. 2023 Zhang et al. 2022a). Numerous studics aim to understand human cognitive processcs, often in highly con- trolled settings. For example, when developing the CIE color model (Wright-Guild experiment), participants were required to look through a small aperture, limiting their field of view to just. 2 degrees. Similarly, in- vestigations of VLMs are frequently conducted in restricted environments (Zhu & Li 2023 Allen-Zhu & Li, 2023). Inspired by prior work, we analyze changes under limited conditions in terms of color, shape, and semantics. Our proposed LENS dataset allows us to do such an analysis.
6 Conclusion
We take a closer look at the fundamental perceptual abilities of VLMs through an eye examination process that focuses on color, shape, and semantics. We introduce the LENS dataset, which instructs the model
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[Using similar colors]
Q: What are GFLOPS at 16 Threads?
A: For 32x32 matrices, the GFLOPS 60. For 160x160 matrices, the GFLOPS is 169. For 480x480 matrices, the GFLOPS is 174. For 960x960 matrices, the GFLOPS is 174.
X1
32
Tiracc
[Using different colors]
2000
1009000
#003000
Q: What GFLOPS 16 Threads?
200
The
A: For 32x32: 8 GFLOPS. For 160x160: 60 GFLOPS.
For 480x480: GFLOPS. For 960x960: 174 GFLOPS
[Using similar shapes]
Onpleyece HenChcator
Q: How does the Employment factor change?
A: Starting 72.9 2008, drops to 63.7 by 2011, continues decreasing 46 in 2014, and slightly increases to 50.4 in 2017.
3M
[Using different shapes]
Q: How does the Employment factor change?
A: 2008 to 2011: Employment increases from to
23.4. 2011 to 2014: Employment rises sharply from 23.4 to 46. 2014 to 2017: Employment slightly
increases from 46 50.4.
Figure 9: Potential Application. We test our findings in the chart reasoning from GPT-4. We vary the color of the chart or the symbol shapes. GPT-4 is a lack of understanding the chart because of improper color and shape. After changing color and shape, we can increase the correctness.
about the examination and ensures that the model is in the appropriate state for the examination. Fine- tuming and evaluating the models on LENS allows us conduct a detailed analysis. The color sensitivity results indicate that VLMs are less responsive the green spectrum. We also observe that the LLM, which acts the brain of the VLM, affects shape sensitivity and the ability to distinguish patch-wisc semantics for recognition. We believe that these insights and the proposed evaluation process will contribute to deeper understanding of VLMs' behavior and improve reasoning capabilities, as shown in the potential application.
Acknowledgments
This work supported by Institute of Information & communications Technology Planning & Evaluation (ITPP) grant funded by the Korea governmen (No. 2022-0-00124, No.RS-2022-1T2201 Develop- ment of Artificial Intelligence Technology for Self- self-Improving Competency- Aware Learning Capabilities; No. 2S-2024--0455882, National AI Research Lab Project; No.RS-2019-I1111990, Artificial Intelligence Graduate School Program(POSTECCt)
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A
Appendix
In this appendix, we include an additional discussion, the details of our LENS and experiments, and addi- tional qualitative results, which are not included in the main paper.
A Impact Statement
As research on Large Language Models (LLMs) and Vision-Langunge Models (VL.Ms) begins, there is a noticeable acceleration in the development speed within the Al industry. This advancement in research has the potential to enrich human life, yet it simultaneously brings rapid changes to our lifestyles. Despite the swift progress in large model research, a gap remains in our understanding of the precise operational principles of these systems. This lack of clarity and control over AT's deep integration into daily life is raising significant social concerns. Our project aims to address these issues, focusing specifically on analyzing the perception mechanisms of VLMs. For this reason, through a better understanding of VLMs by casting a fundamental question, "How do VLMs perceive the world?". However, it is important to recognize that such controllability does not always yield positive effects; it could potentially be exploited for more complex and malicious criminal activities. Currently, our analysis is in its nascent stage, but we hope that our research will contribute to the development of controllable and explainable AI, ultimately fostering a safer cocxistence with AT in the world.
goal is to move towards more controllable system
Additional Discussion
Understanding the inside of AI is difficult because of overparameterization,, non-linear mapping, non-convex optimization, etc. Many researchers have tried to provide and explain the behavior of AT in terms of
theory (Allen-Zhu al. 2019a b), explainable algorithms [Ribeiro et al. 2016 Lundberg & Lee 2017 Wu
et al.
2023 Sundararajan et al. 2017 Petroni et al. 2019 Wu al.) 2020
Selvaraju et al. 2017 physics
law (e.g., scaling low) (Kaplan et al.
2020
Gao et al. 2023a Henighan et al.
2020 Hernandez et al. 2021
Cherti et al.
2023 Bubeck & Sellke
2021 and computational experiments
(Zhang et al. 2017
Prystawski
et al.
2023
Akata et al. 2023).
The approach of deep learning theory explains the phenomena AI in terms of the mathematical form. Thus, it is more rigorous than other approaches. However, deep learning theory includes impractical assumptions such as shallow or infinite-width networks. Explainable AI is to provide an understandable form to users. For example, previous methods have revealed how and why the model's decisions are made. The approach of physies law is to find certain laws that occur in models; the popular law is the scaling law that the larger the model and the more data we use, the better the performance. Similarly, the computational approach exists by observing the model's behavior. Some methods are inspired by philosophy, psychology, and cognitive science because they are based on large language models (LLMs) showing emergent intelligence and reasoning. These approaches might not be rigorous compared to the deep learning theory. Our approach is close to the computational experiments. We tune the pre-trained large model with our dataset and investigated the behavior of the model along with the visual input changes.
C Details of LENS
We propose the eye cxamination process of VLMs to understand how the model perceives the given visual signals. The LENS (Learning ElemeNt for visual Sensory) dataset includes three types of primitive visual information: color, shape, and semantic. The statistics of our LENS are in Table 4 Each LENS data consists of an image, a question with answer options, and a ground truth answer. The questions are randomly sampled from a set associated with each category. In this section, we provide the set of questions for each category of our LENS dataset.
Additionally, Fig. 10, we show samples for the patch group in the semantic category. For the self-swap, two randomly selected patches are swapped within an image. The goal is to find the positions of swapped patches. In contrast, for the cross-swap, single patch is randomly sampled from each image and swapped
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Semantic
Color
Shape
ycs
2
Patch
Train
2,648
6,720
3,500
1,820
3,500 * *3
Validation
568
3,360
1,000
520
1,500 *
Table 4: Statistics of the LENS dataset.
between images. The goal find the position of patch from a different image. For masking, we first randomly sample four patches and replace them with black patches. The goal find the appropriate location for of the sampled patches.
C.1 Color
The list of questions for color dataset in LENS.
Color [Yes or No]
" "Are the color boxes the same color?'
"Do the samples have the same color?"
"There are two sample boxes; are the two samples have the same color?'
Are the two sample boxes painted in the same color?'
Provide an answer on whether the two samples are the same color.'
"Give a short and clear answer to whether the colored boxcs have the same color."
"Share a concise result whether the colors of boxes are the same..
"Offer succinct explanation of whether the two samples are the same color."
Color [SAMPLE 1 SAMPLE 2]
"Fach sample has two colored boxes, which sample has the same color?"
"Each sample has two colored boxes, which sample has the boxes having the same color?"
"Each sample has two colored boxes, which sample has the same colored boxes?"
"Each sample has two colored boxes, which sample has the two boxes painted in the same color?"
"Each sample has two colored boxes, provide an answer on which sample colored with the same color."
"Each sample has two colored boxes, give short and clear answer to what is the sample that has the same color.'
"Each sample has two colored boxes, share concise result of what sample has the same color boxes."
"Each sample has two colored boxes, offer a succinct explanation of which sample has the same color."
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C.2 Shape
The list of questions for shape dataset in LENS.
Shape [Yes or No]
Are the shapes of the two given samples the same?"
Are the two samples identical in shape?"
"Do both samples have the same form?"
"Is the outline of sample matching with that of sample 2?"
Are the shapes of these two samples alike?"
"Is there any shape difference between the two samples?"
"Do the samples share the same geometry?"
Are the forms the two samples equivalent."
The list of questions for shape dataset in LENS.
Shape [SAMPLE
SAMPLE 2]
"Each sample has two shapes, which sample has the same shape?"
"Each sample has two shapes, which sample has the images having the same shape?"
"Each sample has two shapes, what sample has the same shape?"
"Fach sample has two shapes, which sample has the two images having the same shape?"
"aah sample has two shapes, provide an answer on which sample contains the same shape.'
Each sample has two shapes, give a short and clear answer to what sample has the same shape."
Fach sample has shapes, provide concise answer of which sample has the same shape."
"aac sample has shapes, offer answer which sample has the same shape.'
C.3 Semantic
The list of questions for the semantic dataset in LENS.
Semantic [Yes No]
"Do the two samples have the same semantic?"
"Do the samples have the same semantic?"
"There are two samples; do the two samples have the same semantic?'
"Do the two samples have the same semantic?"
"Provide an answer on whether the two samples have the same semantic.'
"Give short and clear answer to whether the two samples have the same semantic."
Provide concise result of whether the same semantic."
"Ofeer succinct explanation of whether the two samples are the same semantic."
Semantic [SAMPLE or SAMPLE 2]
"Each sample has two images, which sample has the closer(or same) semantic than the other?"
"Each sample has two images, which sample has the images having the same(or closer) semantic?'
"Each sample has images, which sample has the closer(or same) semantics than the other?" "Each sample has two images, which sample has the two images having the same(or closer) semantics?'
"Each sample has two images, provide an answer on which sample contains the closer(or same)
semantic than the other."
"Each sample has two images, give a clear answer to which has the same(or closer) semantic.' "Each sample has two images, provide a concise result of which sample has the closer(or same) semantic than the other."
"Each sample has two images, offer succinct explanation of which sample has the same(or closer) semantic."
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C.4 Patch
The list of questions for patch dataset in LENS.
Patch Cross
"Within the 16 divided sections of the image, identify the single patch that originates from
different image.'
"Of the 16 segments in the given image, which one is extracted from an alternate image?"
"There is one patch among the 16 in the image that doesn't ' belong; can you locate it?"
"Spot the section among the 16 patches the image that was taken from a distinct image."
"A single patch out the 16 the provided image is from a different source. Where is it located?"
"One patch in the 16-part divided image is mismatched from another image. Where can it be found?"
"In the image that's split into 16 patches, which section is the outlier sourced from another image?"
"Locate the patch that differs from the rest in the 16-part grid of the image, indicating it's from a different image.'
Patch Self
"Identify the two patches that have exchanged places the 16-patch divided picture.'
"Which two patches in the picture's grid of 16 have been swapped with each other?"
"Locate the two patches whose positions have been reversed in the image composed of 16 patches."
"In the 16-part grid of the image, which two patches
out of their original positions?"
"Find the pair of patches that have been interchanged in the 16-segmented image."
Determine the positions of the two patches that have been swapped in the 16-patch picture.'
"Spot the two patches that aren't in their correct spots within the 16 divided sections of the picture.
"Assess the 16- patch arrangement of the image and identify the two patches that have been swapped."
Patch Masking
"Given the picture divided into 16 patches with some missing, identify where the patch on the right fits.'
"In the 16-segmented picture, where does the separate patch on the right go?"
"Where is the correct position for the patch on the right in the picture's 16-patch layout?"
"Determine where the patch shown on the right should be placed among the missing patches in the picture.
"Regarding the image split into 16 patches with some absent, where should the right-hand patch be located?'
"Find the appropriate spot for the right-side patch in the grid of 16 patches where some are missing.'
"Assess the 16-patch division of the picture and place the patch from the right in its proper location.'
"Where would the patch the right be placed in the fragmented 16-patch picture?'
C.5 Fine- Tuning Setup
We finelune VLMs with our constructed dataset, color, shape, and semantics, respectively. We chose LLaVA [Liu et al., 2023b) because efficient in terms of training time. We employ LoRA (Hu et al. 2022) during the finetuning because is a resource-cfficentt algorithm. We set the training epoch as 2, batch 128, and learning rate 0.0002 with cosine schoduling, Adam optimizer (Kingma Ba 2015) and gradient checkpointing. We use 8 A100 80G GPUs for our experiments.
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The image is divided into 16 patches, with columns labeled from A to D and rows from to 4. For example, (1,1) is al, (1,4) is a4, (4,1) is d1, and (4,4) is d4. Answer the following question based on this: [one of the questions above}
2
Here are options:
OPTION 1: b3, OPTION 2: c2, Answer: none
Answer with OPTION 1 or OPTION 2 or No Answer.
4
(a) Cross-Swap
The image is divided into 16 patches, with columns For example,
labeled from to and rows from (1,1) al, (1,4) is a4, (4,1) d1, (4,4) is d4. Answer the following question based on this: {one of the questions above}
2
3
Here options:
OPTION 1: al and c3, OPTION 2: a1 and a3, No Answer:
none
4
Answer with OPTION 1 OPTION 2 or No Answer
(b) Self-Swap
The image is divided into 16 patches, with columns labeled from A to D and rows from to 4. For example, (1,1) is al, (1,4) is a4, (4,1) is d1, and (4,4) is d4. Answer the following question based on this: {One the questions above)
Patch
Here are options:
OPTION 1: b2, OPTION 2: c3, Answer:
Answer with OPTION OPTION No Answer.
b
d
(c) Masking
Figure 10: QA format for patch dataset in LENS. For every image, randomly sampled one of the questions listed in C.4
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D Color Pattern
In Sec. 3.2 we investigate the visual encoder of VLMs to understand their varying scnsitivity to colors, no- lably the lesser sensitivity to green compared to the other colors. We evaluate CLIP VIT-L/14 336px (Rad- ford et al. 2021 used in LLaVA-v1.5 (Liu et al. 2023a) as described in the main paper. Here, v(-) represents the visual encoder (CLIP ViT-L/14 14 336px). We extract the hidden features, excluding the class token, that are injected into LLMs and average the tokens. We measure the cosine similarity between colors as following: =(Crat)=((Carree))
We choose the similarity measure as cosine similarity because the training method of CLIP uses pairwise cosine similarity. It is computationally infeasible to compare all colors because the number of colors is 256". Thus, we divide R, G, and B into 32 bins which means the number of colors 323 Specifically, the colors are (0, O, 0), (0, 0, 8), ..., (248, 248, 240), (248, 248, 248). Finally, wo apply min-max normalization to the results of cosine similarity to ensure values fall within [0, 1], providing better visualization and interpretation.
sim(Crref Otarget) ||=(aat)]l M[Cuaree 2
Figure 11 shows the similarity patterns between Rod, Green, Blue and the other colors; r-axii represents the converted color value from RGB code. The pattern of Fig. [c is similar to the original green value of The other colored patterns look like an upside-down green shape.
Fig. 11a
0.5
4. 098
0,157
12,205
16,288
20,480
24,576
28,072
30 700
Green value from RGB code
1.0
(b) Similarity between Red and other colors
0.5
4,005
5,102
10:288
16,254
20,450
24,578
28.872
32, 760
Similarity between Green and other colors
1.0
(d) Similarity between Blue and other colors
Figure 11: Color similarity pattern. We extract the color feature from CLIP VIT-L/14 336px which is used for LLaVA v1.5, and then compute the cosine similarity between the reference and target colors. (a): the green value given RGB code. (b), (c), (d): the cosine similarity between the (Red, Green, Bluc} and farget colors. We apply min-max normalization.
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E
Shape
Since the original graph of InstructBLIP is very noisy, we perform polynomial fitting for a more comfortable comparison. As we mentioned in the main paper, InstructBLIP has a similar tendency to LLaVA, i.e., a larger model is more sensitive than a smaller one. In Table 19j the values of SAS (sensitivity area of shape) also indicate consistent results with the graph result.
In Fig. 12 we add the shape sensitivity graph result of InstructBLIP, Fig. 19g 19i
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7B
0.4059
17.047
145.6
LLaVA
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0.3347
14.986
124.6
3B
0.2084
10.73
80.4
InstructBLIP
7B
0.1662
9.74
69.6
(j) Sensitivity Area of Shape (SAS)
Shape sensitivity. We measure the sensitivity of LLaVA and InstructBLIP between circle and target shapes by varying (a) the eccentricity of circle, (b) the number of vertices in regular polygon, or (c) the size of a circle. The graph (d-f) is the result of LLaVA, and (g-i) is of InstructBLIP. The model is more sensitive if the score changes at (d, g) lower cecentricity, (c, h) a higher number of vertices, and (f, i) a narrower range of size. The results shows that a larger VLM is more sensitive than a smaller one. (j) SAS quantifies the shape sensitivity, and shows consistent results with the graph results.
Figure 12:
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F Patch Analysis
As mentioned in the main paper, provide the reference image and target cropped patch VLM, ask if the given samples are the same semantically, and visualize the score map. Figure 13 shows the results. Let the original image size be 1536; then, we vary the patch size from 260 to 60 with fixed stride size.
Same Class
Same Class
Different Class
Different Class
7B
138
138
13B
Figure 13: Additional patch analysis result, As mentioned in the main paper, we vary the patch size. The upper row has a smaller patch size, and the lower row has a bigger onc. As the patch size increases, the score map becomes thicker because the patch contain the object part.
G Examination without Fine-tunning
Recent advances in VLMS demonstrate their generalization and capabilities. For example, InterVL.2.5 (Chen) et al. 2024a b b
compare and analyze multiple images. Given the capabilities of these recent models, we provide the examination results without additional fino-tuning.
G.1 Examination: Color
Table and Figure 14 show the SAC results of InternVL2.5. Similarly, SmolVLM-IIntruuc shows the same tendency shown in Table and Figure 15
As shown in Table 7 DJwen--VT--BBInsttmne demonstrates
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InternVL 2.5
Green
Blue
Table 5: Sensitivity Area of Color (SAC) of InternVL2.5.
Color Wheel
4B
Model
Size
Red
Green
Blue
2B
0.1144
0.3953
0.1948
InternVL2.5
40
0.0772
0.1827
0.1026
8B
0.0740
0.2023
0.1121
8B
Figure 14: Visualization Color sensitivity of InternVL2.55
SmolVLM Instruct
Color Wheel
Red
Green
Blue
Table 6: Sensitivity Area of Color (SAC) of SmolVLM Instruct.
Model
Size
Red
Green
Blue
SmoIVLM
Instruct
0.2487
0.8676
0.2652
Figure 15: Visualization Color sensitivity of SmolVLM In- struct.
high SAC value for green, whereas Qwen2-VL-2h V1-22--V--BB-nstruee does not. To investigate this discrepancy, we visualize color sensitivity shown in Fig. [6] We observe that, while the green area is not particularly narrow, its score value is lower than other colors. To remove the scoring intensity and compute the area, we compute the hard version of SAC as follows:
Sensitivity Area of Color =
/ [[ (Crof Ctarget) 2 0.5) dCtarget>
(3)
where [[|] is indicate function. The values are 0.1206, 0.2760, and 0.2780 for red, green, and blue, respectively. The green and blue values are comparable.
We conduct additional experiments with the different visual encoders. Specifically, given a visual encoder, such CLIP SigLIP, measure the similarity score between the target color image and the reference text "A photo of red, green, blue," instead of the reference color image. Table g shows the similarity score of the visual encoder. We observe that the visual encoder perceives the green color broadly.
G.2 Examination: Shape
We visualize the score of Qwen2-VL. and SmoIVLM (Sec Fig. 18 Appendix). The Qwen2-VT. 2B model and SmolVLM Base exhibit limited comparative capabilities, their responses yield similar scores even when the inputs different. Figures 18 and 19 show the SAS results. In the main paper, VLMSs tend to be
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Qwen2 VL
Red
Green
Blue
Color Wheel 2B
Table 7: Sensitivity Area of Color (SAC) of Qwen2-VIL VL.
Model
Size
Red
Green
Blue
1.0727
0.9949
1.0868
Qwen2 VL
0.1108
0.3920
0.1592
Figure 16: Visualization Color sensitivity of Qwen2- VL. LL.
Red
Color Wheel
CLIP
Table 8: Color similarity score in vi-
sual encoder.
Model
Red
Green
Blue
SigLIP
CLIP
0.7534
0.7816
0.7807
SigLIP
0.1333
0.1814
0.1733
Visualization color similarity score in visual encoder. We compute the similarity score between the phrases "the photo of red, green, blue" and the corresponding target col- ors.
Figure 17:
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more sensitive than smaller ones. Similarly, for InternVL2.5, the larger models generally are sensitive compared to the smaller ones. Qwen2-VT. also shows similar overall tendency. For SmolVLM, the Instruct model shows higher sensitivity than the base model, which we attribute to its enhanced ability to follow the given instructions. The Qwen2-VL 2B model and SmolVLM Base exhibit limited comparative capabilitios, as their responses yield similar scores even when the inputs are different.
Ref.
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88
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(l)) Polygon sensitivity (-)
ii) Size sensitivity (-x+)
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(k) Polygon sensitivity (++)
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Model
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Size
2B
0.6915
17.115
193.7
InterVI 2.5
4B
0.4000
10.126
117.1
8B
0.3739
10.997
70.5
(m) Sensitivity Area of Shape (SAS)
Figure 18: Shape sensitivity of InterVL 2.5. quantifies the shape sensitivity.
The graph (d-l) is the result of InternVL2.5 (m) SAS
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Ref.
Ref.
Ref.
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Qwen2 VL
7B
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49.3
Base
0.8544
25.785
191.7
SmolVLM
Instruct 0.4192
8.174
42.9
(j) Sensitivity Area of Shape (SAS)
Figure 19: Shape sensitivity of Qwen2-VL and SmolVLM. The graph (d-f) is the result Qwen2-Vi VL. The graph (g-i) is the result SmolVLM (j) SAS quantifies the shape sensitivity.
G.3 Examination: OCR
We vary the font. size within the range of 1 100 and assess the recognition performance based on these variations. Accuracy is computed by the exact match. Table shows that the larger models are better than the smaller models. Also, we observe that the error occurs when the font size is small. If the font size is large, the models recognize it well.
G.4 Chart
We design a small chart understanding dataset to provide the quantitative results. The colors in the chart arranged from lowest to highost contrast. At low contrast, humans have difficulty distinguishing between labels, but as the contrast increases, the distinction becomes easier. We analyzed whether this characteristic also works in VLMs. In our experiments, the InternVL2.5 4B model starts to answer questions correctly at 80% intensity contrast, and the Qwen2 VL. 7B model starts to answer correctly at 60% contrast. The results show that the higher the color contrast the chart, the better the model understands the chart.
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Table 9: OCR ability by varying font size. We measure the accuracy of OCR performance. We vary the font size from small to large. Models wrong when the text is small.
Model
Model Size
Accuracy
2B
97.0
InterVL 2.5
4B
97.1
8B
97.3
2B
96.7
Qwen2 VL
7B
97.1
InternVL 2,5 48
Qwen2 VL 78 Instruct
Figure 20: Chart understanding by varying contrast. We observe that the contrast important improve the chart understanding of VLMs.
28


Reviews:
[{"review_id": "vDSmZNLe9o", "reviewer": "Reviewer_A6mV", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n1. The idea of measuring sensitivity towards color, shape, and semantics is interesting.\n2. The results of sensitivity of VLMs towards green are interesting, since this is different from human’s perception pattern.\n3. The application on chart understanding is straightforward and looks promising.\n4. The paper is well-written and easy to understand.\n\n\n**Weaknesses**\n1. Table 1 lacks a human baseline to compare with in order to understand the visual perception abilities of VLMs compared with humans. The human baseline can be more interesting for color sensitivity because it shows a different pattern between human perception and VLM’s perception. Also, Table 1 should highlight the rows which stand for “no finetuning” and “with finetuning”.\n2. Figure 3 misses a human baseline. This can demonstrate how humans perceive colors in a straightforward way.\n3. In Section 3.2, do the reference images contain only the red or green or blue pixels?\n4. For color sensitivity analysis (Section 3.1 and Figure 3), the analysis mainly focuses on the whole VLMs. The work can also study the visual perception of the visual encoders by measuring cosine similarity scores between reference color and target color from the visual encoder’s outputs. \n5. Similar to the above. For color sensitivity analysis (Section 3.2), when computing the similarity scores, the v(.) score can be extended to the last layer hidden representations of image patches from the LLM (you can ignore the text sequence and pass in only the image patches to the LLM for this). In this way, the work can compare and see the impact of LLMs during color perception.\n6. In Table 1, it seems that larger InstructBlip cannot outperform smaller ones across multiple tasks. Why does this happen?\n7. I understand that finetuning can let the VLMs know how to compare two sample images better. However, this goes against with the idea of understanding the perception abilities of VLMs, since the model’s perception abilities may improve during the finetuning, and thus the current results (based on finetuned VLMs on LENS) cannot demonstrate the visual perception abilities of the off-the-shelf VLMs. Can we run the experiments with off-the-shelf VLMs?\n8. For Figure 2, the work can add the analyses based on just the visual encoders to reflect how LLM impacts the visual processing. The work can compute cosine similarity scores between the source image and the target patch to make the plots. Plus, it will be meaningful to add the predicted class labels of each source image to see when the model assigns higher scores to the background, will the model fail to recognize the object in the image.\n10. Is it possible to adopt the findings to a small chart understanding benchmark to better demonstrate the potential of application on chart understanding? This can quantitatively demonstrate the potential of application of the findings.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Vision language models (VLMs) have shown impressive performance on a wide range of benchmarks. However, it remains unclear about the visual perception abilities of these VLMs. This work introduces LENS dataset to measure the perception sensitivities of VLMs on colors, shapes, and semantics. Based on the results, LLaVA and InstructBLIP are less responsive to green, which is different from human perception (i.e., humans are more sensitive to green, among red/green/blue). Also, the results demonstrate that the LLM backbones are important components for shape and semantic sensitivity. Last, the work shows an application potential on chart understanding based on the observed patterns.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. [Minor] Missing citation [1] in section 3.2.\n\n[1] Patel, Roma, and Ellie Pavlick. \"Mapping language models to grounded conceptual spaces.\" International conference on learning representations. 2022.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "8UqXPsje2h", "reviewer": "Reviewer_6s3j", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## Strengths\n\n1. The motivation of this paper is clear and highly valuable. Most existing works investigate vision-language models as a whole, often overlooking the impact of visual perception abilities on overall performance.\n2. The experimental designs to evaluate the sensitivity to color, shape, and semantics are novel and intriguing, and the results are comprehensive.\n3. The paper is well-written and easy to read.\n\n## Weakness\n\n1. The procedure for fine-tuning a VLM and performing readiness checks is quite cumbersome, which may limit the adaptability of the proposed evaluation approach to other models.\n2. Furthermore, the choice of evaluated VLMs, such as LLaVA and InstructBLIP, is not ideal. Most existing state-of-the-art models, such as Qwen2-VL and InternVL2, already possess prior information about color, shape, and object semantics. Additionally, they can follow the instructions in the proposed dataset.\n3. Some of the formulas are quite misleading. For example, in Section 3.2, the formula \n$\\[ I_{x,y} = (255, 0, 0) \\cdot \\text{sim}(c_{\\text{Red}}, I_{x,y}) + (0, 255, 0) \\cdot \\text{sim}(c_{\\text{Green}}, I_{x,y}) + (0, 0, 255) \\cdot \\text{sim}(c_{\\text{Blue}}, I_{x,y}) \\]$\nis problematic. Since $\\(\\text{sim}(x, y)\\)$ is a scalar, it is unclear how this formula results in an image.\n4. Regarding the questions in the dataset, the paper mentions that they are randomly sampled from a set. Could the authors provide detailed information about this set?\n5. As the title of this paper suggests, it focuses on the visual competency of vision-language models. However, besides color, shape, and semantics, the paper overlooks an important aspect: optical character recognition (OCR) ability. The authors are encouraged to investigate this aspect as well.\n6. In Section 3.1, the statement ``Considering the vast size of the color space, \\(256^3 \\approx 16.8M\\), we reduce the color space to \\(32^3 = 32,768\\)`` needs clarification. What does this reduction entail?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper primarily investigates the visual capabilities of a vision-language model from the perspectives of color, shape, and semantics. To achieve this goal, a series of meticulously designed questions targeting these three aspects are presented. Additionally, specific metrics, such as SAC and SAS, are developed to evaluate the sensitivity to color, shape, and semantics. Following this, a detailed analysis of the results is provided. Overall, the motivation of this paper is clear, and the experiments are comprehensive.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See weakness", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "gUMeZ15VYp", "reviewer": "Reviewer_Pg4G", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths\n1. **Novel Evaluation Framework**: The proposed \"eye examination\" methodology offers a systematic and comprehensive approach to assess the visual capabilities of Vision-Language Models (VLMs). This framework draws inspiration from human vision tests and provides a structured way to evaluate VLMs' understanding of visual elements such as color, shape, and semantics.\n\n2. **LENS Dataset**: The introduction of the LENS (Learning Element for visual Sensory) dataset is a valuable contribution. This synthetic dataset, specifically designed for instructing and assessing VLMs, categorizes visual elements into color, shape, and semantics. The dataset enables the models to understand the examination process and allows for a standardized evaluation of their visual competencies.\n\n3. **Quantitative Metrics**: The authors define and calculate quantitative metrics, such as Sensitivity Area of Color (SAC) and Sensitivity Area of Shape (SAS), to measure VLMs' sensitivities to different visual elements. These metrics provide a clear and objective way to compare the performance of different models and understand their strengths and weaknesses.\n\n4. **Insightful Experimental Findings**: The paper presents several interesting experimental findings that shed light on the behavior of VLMs. For instance, the authors discover that VLMs exhibit varying sensitivities to different colors, with a consistent insensitivity to green across models. They also find that larger VLMs are more sensitive to subtle shape differences compared to smaller models. These insights contribute to a deeper understanding of how VLMs perceive and process visual information.\n\n5. **Potential Applications**: The authors discuss potential applications of their findings, such as improving VLMs' performance in chart image understanding by pre-processing the visual input based on the model's sensitivities. This demonstrates the practical relevance of the research and highlights how the proposed methodology can be used to enhance the performance of VLMs in real-world scenarios.\n\n### Areas for Improvement\n1. **Limited Model Coverage**: While the paper evaluates two popular VLMs (LLaVA and InstructBLIP), it would be beneficial to extend the analysis to a wider range of models. This would provide a more comprehensive understanding of the visual competencies across different architectures and training approaches.\n\n2. **Robustness Analysis**: The authors could consider investigating the robustness of the proposed metrics and findings under various conditions, such as different lighting, noise levels, or image resolutions. This would help assess the generalizability of the results and identify potential limitations of the evaluation framework.\n\n3. **Comparison with Human Vision**: Although the authors mention that VLMs' insensitivity to green differs from human vision, a more detailed comparison between VLMs and human visual perception could be informative. Exploring the similarities and differences between the two could help identify areas where VLMs need improvement to better align with human-like visual understanding.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The submission introduces a methodical framework dubbed the \"eye examination\" for assessing the visual capabilities of Vision-Language Models (VLMs). Here are the core contributions and advancements outlined in this paper:\n\n1. **Examination Protocol**: The authors propose a structured examination protocol consisting of instruction, readiness check, and examination steps to assess the visual recognition abilities of VLMs.\n2. **LENS Dataset**: The authors create the LENS (Learning Element for visual Sensory) dataset, which is designed for training and assessing VLMs' visual competencies in the areas of color, shape, and semantics.\n3. **Experimental Findings**: Through extensive experiments, the authors reveal interesting insights into VLMs' sensitivities towards different visual elements, such as color, shape, and semantics.\n4. **Potential Applications**: The findings of this research can inspire future improvements in VLMs and their applications in various domains, such as chart image understanding.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The current evaluation of the two models, Llava and InstructBlip, is somewhat outdated and does not represent the latest advancements in the field. The authors are encouraged to assess more recent models such as GPT-4O, Claued35, or open-source models like Qwen2-VL/InternVL. It would be particularly interesting to see if these newer models exhibit similar patterns.\n\nAdditionally, the authors have not explored the differences among various vision encoders. Investigating whether models based on the SigLIP encoder, for instance, do not exhibit the patterns identified in this paper could provide valuable insights.\n\nIn Section 4, the discussion on potential applications raises questions about whether these applications could be extended to more powerful models. It would be beneficial to establish criteria for what types of model alterations could consistently enhance performance on images.\n\nThe exploration in Section 3.5, \"Examination: Semantics,\" is intriguing. I am curious why larger LLMs seem to better comprehend semantic information under the same vision encoder conditions. However, I find the experiments presented insufficient; the semantic score map only indicates that larger LLMs perform well without explaining why that is the case. The authors might consider providing deeper insights, such as whether the features of large LLMs are more analogous to image features or exhibit other similarities.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Reviewers were unanimous that this paper should be accepted, with the majority of their concerns resolved during the discussion period.
Decision: Accept (poster)