Title: Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Venue: NeurIPS 2024
Accepted: yes
Keywords: visual reasoning

foundation models

multi-object reasoning

cognitive science
Abstract: Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks -- such as counting, localization, and simple forms of visual analogy -- that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.

Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem

Abstract

Recent work has documented striking heterogeneity in the performance of stateof-the-art vision language models (VLMs), including both multimodal language models and text-to-image models. These models are able to describe and generate a diverse array of complex, naturalistic images, yet they exhibit surprising failures on basic multi-object reasoning tasks - such as counting, localization, and simple forms of visual analogy - that humans perform with near perfect accuracy. To better understand this puzzling pattern of successes and failures, we turn to theoretical accounts of the binding problem in cognitive science and neuroscience, a fundamental problem that arises when a shared set of representational resources must be used to represent distinct entities (e.g., to represent multiple objects in an image), necessitating the use of serial processing to avoid interference. We find that many of the puzzling failures of state-of-the-art VLMs can be explained as arising due to the binding problem, and that these failure modes are strikingly similar to the limitations exhibited by rapid, feedforward processing in the human brain.

1 Introduction

Recent progress in training large-scale neural networks on internet-scale datasets has led to the creation of AI systems with capabilities rivaling human performance across a broad range of complex tasks. Most recently, this has given rise to an array of vision language models (VLMs), including multimodal language models such as GPT-4v that can generate text descriptions of multimodal text and image inputs [1], and text-to-image models such as DALL-E 3 that can generate images from natural language descriptions [24]. However, despite the considerable success of VLMs across many tasks, these models still perform poorly on several surprisingly simple multi-object reasoning tasks such as counting [23, 25, 40], relational image generation [7], relational scene understanding [15, 31], and simple visual analogy tasks [20, 38] - on which humans achieve near perfect accuracy.

Drawing from theoretical work both in cognitive science and neuroscience, we turn to the binding problem [9, 10, 29, 33, 36] as a potential explanation for these limitations. 'Binding' refers to the ability to associate one feature of an object (e.g., its color) with the other features of that object (e.g., its shape and location), and the 'binding problem' refers to the question of how the brain accomplishes this without interference between the features for different objects. It is widely recognized that the

human visual system relies on serial processing to solve this problem, iteratively directing attention to individual objects so as to avoid interference [28, 33], and that binding errors arise when it is forced to rely on rapid, parallel visual processing [14, 19, 33]. For example, when human participants are not able to effectively deploy serial processing (e.g., because attention is overloaded, or because speeded judgments are required), they are susceptible to so-called illusory conjunctions (e.g., mistakenly identifying a red square in an image that contains a green square and a red circle) [32].

In this work, we test the hypothesis that the failures exhibited by VLMs on multi-object reasoning tasks are due to representational interference resulting from an inability to manage the binding problem. We first investigate two classic tasks from the cognitive science literature, visual search [33] and numerical estimation [14, 19] (i.e., counting), finding that a wide range of VLMs (including 5 multimodal language models and 4 text-to-image models) exhibit stark capacity constraints similar to those displayed by human observers when forced to make speeded responses. Importantly, although these effects are more pronounced for scenes with more objects, they cannot be explained entirely as a function of the number of objects in a scene. Instead, we find that performance is best explained by the probability of interference given the specific distribution of features and their conjunctions within a scene. Motivated by this observation, we develop a novel scene description benchmark that systematically varies the likelihood of interference, finding that this quantity is highly predictive of binding errors.

We also apply these insights to better understand the limitations of VLMs in visual analogy tasks, introducing a simple input pre-processing technique to reduce the potential for representational interference. We show that this technique improves the performance of VLMs on the task, suggesting that their original limitation on this task may be due to a more basic difficulty with processing multi-object scenes, rather than an inability to process relations. Finally, we discuss the normative factors that underlie the binding problem [2, 9, 22], highlighting the role of compositional representations, which are useful for generalization, but introduce the potential for interference when shared representations are used to process multiple objects at the same time. We argue that, surprisingly, the binding failures exhibited by VLMs imply the presence of compositional representations. Overall, these results highlight the usefulness of cognitive science in helping to understand the limits of large-scale generative models, and suggest the presence of a common set of principles that govern information processing in both artificial systems and human cognition.

2 Visual Search

Extensive prior work in cognitive psychology has tested how people process scenes involving multiple objects and under what conditions their performance degrades. These studies demonstrate that performance is not driven solely by the number of objects present in a scene, but also depends on the likelihood of interference among objects given the specific distribution of features and feature conjunctions from which they are composed. This can be seen most directly in research on visual search, where participants are typically tasked with identifying a specific object within a multi-object array. A classic pattern of results arises from a comparison of two conditions: disjunctive and conjunctive search [33]. In disjunctive search (depicted on the left side of Figure 1), the array consists of distractor objects that share one feature with the target (e.g., the distractors are all circles) but differ in a second feature (e.g., the distractors are all red circles in the 2D task variant). Since one of the feature values (the color green) is uniquely assigned to the target object, the distractors present little interference and therefore task performance is invariant to the number of distractors. This condition is therefore sometimes referred to as 'popout' search, as the target immediately stands out from the distractors, and the task can thus be performed rapidly without the need for serial processing. Conversely, in conjunctive search (depicted in the middle of Figure 1), there are two types of distractor objects that each share one feature with the target (e.g., half of the distractors are red L-shapes and the other half are green T-shapes). In this case, the target (a green L-shape) possesses no unique feature that easily distinguishes it from the distractors, leading to a significant degree of interference between the distractors and the target. One way to mitigate this is the use of serial search to identify the target. This is suggested by ubiquitiously observed increases in reaction time as a function of the number of distractors, as well as the observation that when participants are prevented from engaging in serial search (e.g., by forcing participants to respond quickly), task performance degrades rapidly as more objects are added to the scene.

Disjunctive search

Conjunctive search

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2.1 Methods

We tested the extent to which VLMs demonstrate similar capacity constraints to humans in visual search tasks. We evaluated four multimodal language models - GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5 - on a task involving disjunctive and conjunctive search conditions. 1 We generated datasets involving either 2D sprites or 3D scenes created in Blender [6] (similar to those found in the CLEVR dataset [13]). The datasets were designed to evaluate the ability of the model to detect the presence of a target object among multiple distractors. In half of the images, a target was present, while in the other half, no target was present.

Each image contained between 4 and 50 distractors. For the disjunctive search task, these consisted of non-overlapping red circles (for the 2D dataset) or green spheres (for the 3D dataset) of a uniform size. Half of the images additionally contained a target object, which was a green circle (for the 2D dataset) or a red sphere (for the 3D dataset). For the conjunctive search task, the 2D dataset consisted of images in which the distractors were either red L-shapes or green T-shapes (randomly selected with equal probability). Half of the images additionally contained a target object, which was a green L-shape. The 3D dataset consisted of images in which the distractors were either green spheres or red cubes (randomly selected with equal probability). Half of the images additionally contained a target object, which was a red sphere. Each of the datasets (2D disjunctive, 2D conjunctive, 3D disjunctive, 3D conjunctive) contained 1000 images.

2.2 Results

We measured the performance of each model by calculating, for each condition, how detection accuracy varied as a function of the number of distractors 2 . The results indicate that performance in the disjunctive search (i.e., popout) condition was perfect, and invariant to the number of distractors. That is, regardless of the number of distractors, all models achieved perfect accuracy in this condition.

In contrast, in the conjunctive search condition performance was inversely related to the number of objects: for 5 objects, all models displayed an accuracy of ∼ 90%, but as the number of objects increased, performance dropped substantially. These results were consistently observed for both the 2D (top panel of Figure 1) and 3D (bottom panel of Figure 1) datasets. These results were also replicated in an alternative version of the disjunctive search task, in which target and distractor colors were varied between trials (Supplementary Figure 7).

The results of these experiments suggest that multimodal language models demonstrate human-like capacity constraints in their ability to perform visual search in multi-object settings. It is important to emphasize that these capacity constraints are not driven solely by the number of objects present within a scene. Like humans, these models demonstrate capacity constraints only in the task conditions that are impacted by interference between the target and distractor objects, consistent with the hypothesis that these capacity constraints arise as a consequence of the binding problem.

3 Numerical Estimation

To assess the generality of the human-like capacity constraints observed for VLMs in visual processing, we investigated a simple numerical estimation task (i.e., counting) that has been widely studied in cognitive psychology. Although human observers can precisely count a very large number of items when allowed to explicitly process those items one at a time, their ability to rapidly estimate the number of items in a display is subject to a severe capacity constraint. Studies have found that the number of objects that can be reliably estimated without explicit serial counting (sometimes referred to as 'subitizing') is somewhere between 4 and 6 [14, 17, 19, 27, 34]. To determine whether VLMs are subject to similar constraints, we evaluated both multimodal language models (GPT-4v, GPT-4o, Gemini Ultra 1.5, Claude Sonnet 3.5, and Llava 1.5) and text-to-image models (Stable Diffusion Ultra, DALL-E 3, Google Parti, and Google Muse) on a numerical estimation task involving variations of both the number and type of objects. We found that VLMs, across a variety of stimulus and model types, display strikingly similar quantitative capacity limits to those observed in human vision. We also found that these capacity constraints were strongly affected by the variability of features present in an image. This effect is consistent with the hypothesis that these constraints arise due to representational interference: given that objects are represented with a shared set of representational resources, greater feature variability leads to less overlap in the use of these resources, and therefore less opportunities for interference and binding errors.

3.1 Methods

We generated datasets involving both 2D sprites and 3D objects, varying the number of objects per image between 1 and 20. We explored four conditions with varying levels of feature entropy (i.e., feature variability): a low-entropy condition in which all objects in an image had the same color and shape; two medium-entropy conditions in which all objects in an image had the same shape but unique colors, or vice versa; and a high-entropy condition in which all objects in an image had unique colors and shapes. We prompted the multimodal language models to describe the image and then state the number of objects present in it. To test the text-to-image models, we generated a dataset comprising 100 distinct categories, evenly split between common foods (50 categories) and animals (50 categories). We tasked these models with generating images from each category, for which the number of instances of each object ranged from 1 to 10. To assess their ability to generate images with the exact number of objects requested, we conducted a human evaluation study. Participants were asked to count and report the number of objects visible in each generated image. The collected human judgments were then used to quantify the model's accuracy. See Appendix C for further details on human evaluations.

3.2 Results

We measured performance by calculating, for each condition, how accuracy varied with the number of objects present in the scene. The results indicated that, regardless of the type of stimuli used (2D vs. 3D shapes, or animals vs. food), and across two fundamentally different types of vision language model (multimodal language models and text-to-image models), VLMs displayed human-like capacity limits (Figure 2). For both multimodal language models and T2I models, accuracy was very high for scenes involving a relatively small number of objects (1-5), but dropped sharply for scenes involving

2D Sprites

3D Objects

High entropy

Medium entropy

Low Entropy

Foods

Animals

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VLMs

T2Is

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6 or more objects. Moreover, the multimodal language models exhibited performance consistent with our hypothesis that capacity limits arise due to representational interference across objects (i.e., the binding problem), with overall performance highest in the high-entropy condition (lowest interference), lowest in the low-entropy condition (highest interference), and intermediate in between these two extremes in the medium-entropy conditions. Though there are slight differences between the capacity limits exhibited by these two classes of models, it is striking that they both fall within the subitizing limit of human vision, especially when considering the significant differences in both architecture and training procedures. Furthermore, the effect of feature entropy on these capacity

limits strongly suggests that they are driven by representational interference, arising due an inability to manage the binding problem. To further investigate this hypothesis, we next turned to a novel scene description benchmark that allowed us to systematically investigate the likelihood of representational interference, thus enabling a direct test of the extent to which this factor is responsible for the shortcomings of VLMs.

4 Scene Description

Theoretical accounts of the binding problem [9, 33] posit that capacity limits in rapid visual processing arise as a consequence of interference between representations. Given a scene containing multiple objects, and a set of shared features with which to represent those objects, the likelihood of interference will tend to increase as a function of the number of objects in the scene (without the availability of a mechanism for binding features together, e.g., serial processing). However, as emphasized in our experiments on visual search and numerosity estimation, interference is not driven solely by the number of objects, but is also strongly influenced by the specific feature conjunctions present within a scene.

We developed a novel scene description task to further investigate the extent to which VLM performance is driven by representational interference. The task is illustrated in Figure 3a. For each image, the likelihood of representational interference was quantified as the number of feature triplets present in that image. A feature triplet is defined as any set of three objects for which one pair shares a feature, and another pair shares a different feature. For instance, {green X, green triangle, yellow triangle} is a feature triplet, because the feature 'green' is shared by two objects (the green X and the green triangle), and the feature 'triangle' is shared by two objects (the green triangle and the yellow triangle). Without the ability to accurately bind these features together at the level of objects, such feature triplets create opportunities for representational interference, and thus lead to illusory conjunctions. For instance, the feature triplet {green X, green triangle, yellow triangle} may lead to the erroneous identification of a yellow X. We studied the extent to which the presence of such feature triplets can account for scene description performance in VLMs.

4.1 Methods

As in the previous tasks, we generated datasets involving either 2D sprites or 3D objects. Each scene contained a variable number of objects (10-15 objects for the 2D dataset and 8-12 objects for the 3D dataset), and we systematically varied the number of feature triplets present in each scene. For example, the scene depicted in Figure 3a contains three feature triplets (illustrated by the dashed lines). VLMs (GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5) were prompted to provide a description of the objects in JSON format (see Appendix B for more details). We also generated prompts describing similar scenes (but involving real-world objects) and tested the ability of the T2I models (Stable Diffusion Ultra, DALL-E 3, Google Parti, and Google Muse) to accurately generate these scenes (as assessed by human evaluation; see Appendix C for more details). To obtain a representative sampling of scenes with different triplet counts, we systematically varied the diversity of colors and shapes across trials. This approach ensured adequate sampling of trials with different feature combinations and their associated triplet counts. To ensure reliable performance estimates, we excluded from analysis any triplet counts represented by fewer than 20 trials across all conditions. For the T2I experiments, we additionally excluded trials where models generated more than three extraneous objects not specified in the prompt, as these represented significant deviations from the intended scene structure.

4.2 Results

We measured scene description performance by calculating how the number of errors (quantified as the edit distance between the true description of the scene and the model's description of the scene) varies as a function of the number of objects present in the scene, and the number of feature triplets. The results (Figure 3) confirmed our prediction that performance should vary as a function of the number of triplets. Across multiple stimulus types (2D and 3D objects), and model types (both multimodal language models and text-to-image models), the largest number of errors occurred in the trials where the risk of binding errors was highest (i.e., the trials with the largest number of feature triplets), consistent with the hypothesis that errors would be driven primarily by the formation

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of illusory conjunctions. These results also showed a pattern of increasing errors as a function of the number of objects, consistent with a general capacity limit on the number of objects that can be accurately represented at the same time. Overall, these results suggest that the capacity limits displayed by VLMs are best explained by an inability to manage the binding problem. In the next section, we apply these insights to better understand the limited visual reasoning capabilities of VLMs.

5 Visual Analogy

An open question in studying the performance of VLMs is the extent to which these models can solve analogical reasoning tasks. These tasks are of particular interest given their centrality in human higher-order cognition [11] and their use as measures of human intelligence [30]. Recent work has demonstrated that LLMs have an impressive ability to solve a range of text-based analogical reasoning tasks [37], but initial tests of VLMs have suggested that they often struggle to solve comparable visual forms of these tasks, sometimes performing well below human participants [20, 38].

This leads to the question of why, given the success of LLMs on text-based problems in this domain, VLMs do not display comparable success in solving analogy tasks. One possible explanation,

Unified Condition

Decomposed Condition

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Target Pair 1

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suggested by our investigation of the binding problem, is that the difficulty displayed by VLMs on visual reasoning tasks may stem from a more basic difficulty with processing multi-object scenes. In other words, regardless of whether VLMs have the capacity for abstract reasoning necessary to solve analogy tasks, they will likely struggle to solve visual analogy tasks simply because these tasks involve processing multi-object scenes. To distinguish between these two failure modes (inability to process abstract relations vs. inability to process multi-object scenes), we performed experiments using a visual analogy task in which the visual processing demands were explicitly manipulated, and measured the ability of multimodal language models to perform both abstract relational tasks and basic object-level tasks.

5.1 Methods

We generated 200 trials from a simple relational match-to-sample (RMTS) task (Figure 4) using the same 2D sprite stimuli from the previous experiments. We selected a subset of 8 easily recognizable shapes and colors to generate a set of 64 stimuli. For each trial, we chose the source pair by sampling two objects that shared at least one of the two feature dimensions. We then selected the target pairs by sampling two pairs of objects: one which matched the source pair exactly along its relations (the correct target) and one which shared only one of the relations with the source pair (the incorrect target). We manipulated visual processing demands by investigating two conditions, one in which the source and target pairs were presented in a single image (the 'unified' condition), and one in which the source and target pairs were presented as separate images presented in sequence (the 'decomposed' condition), thereby reducing the chance of binding errors.

We assessed the performance of four VLMs (GPT-4v, GPT-4o, Gemini Ultra 1.5, and Claude Sonnet 3.5) in four tasks: identification of the correct target pair in the full RMTS task (Analogy), decoding of single features from specific individual objects (Single Feature Decoding Task), comprehensive decoding of all features in a given problem (Full Feature Decoding), and decoding of relations between object pairs (Relation Decoding).

5.2 Results

We found that performance on this task was highly variable aross VLMs, with some models (Claude Sonnet) showing nearly perfect performance on all tasks (Table 3) and other models (Gemini Ultra) showing poor performance on most tasks (Table 4). These results are consistent with recent work showing mixed success on visual analogy problems [38], and at odds with work claiming that VLMs have no capacity for visual analogy [20]. Interestingly, we also found that many models also struggled on more basic tasks such as identifying the features of the objects present in the image, or identifying the relations for individual pairs of objects.

Most importantly, we found that performance was significantly improved in the Decomposed condition (involving separate images for each pair of objects) as compared with the Unified condition (involving a single image with all object pairs). This was the case across all tasks, and for all models, except for the cases in which performance was already at ceiling for the Unified condition. Taken together, these results suggest that the poor performance of VLMs on visual analogy tasks may be a consequence of a more general limitation with processing multi-object scenes, arising due to an inability to manage the binding problem.

6 Discussion

We have presented a series of experiments aimed at understanding the limits of vision language models in processing multi-object scenes. Our results suggest that these limitations can all be understood as arising from an inability to manage the binding problem, a fundamental problem associated with compositional coding identified by classic work in cognitive science [10, 33].

Recent theoretical work has formalized this problem within a normative framework [9], suggesting that it arises due to a tension between the learning of compositional representations, and the shared use of such representations to encode multiple objects at the same time. To illustrate this, consider two different schemes for representing multi-object scenes: a conjunc-

tive scheme, involving dedicated representations for every possible conjunction of features (e.g., { redsquare , greencircle , bluetriangle , greensquare , . . . } ) vs. a compositional scheme, involving the dynamic combination of shared features across different dimensions (e.g., combining the features { red } and { triangle } to represent a red triangle). The compositional scheme enables efficient use of finite neural resources, and offers major benefits in terms of generalization (e.g., anything learned about red triangles can then be readily generalized to support inferences about other red objects), but, without a mechanism for dynamically keeping track of the bindings between features, this scheme leads to severe interference, giving rise to the capacity limits observed in cognitive processing. One surprising implication of this perspective is that the binding errors exhibited by VLMs suggest that they have developed compositional representations, perhaps as a consequence of being forced to generalize by their immense and highly diverse training corpora. Without the use of such compositional representations (i.e., if VLMs employed a conjunctive coding scheme), there would be no interference between the representations for different objects, and thus there would be no binding problem. The presence of the binding problem, therefore, implies the presence of compositional representations in VLMs.

It is worth considering how VLMs might be improved so as to enable them to cope with the binding problem. One might think that this could be accomplished through additional fine-tuning on multiobject tasks. However, the theoretical perspective outlined above suggests that, to the extent this alleviates the binding problem, it would do so by eliminating the use of compositional representations, which would then have negative impacts on generalization. The question then is how VLMs might be enhanced to solve the binding problem, while preserving the benefits of compositional representations . The most obvious possibility here is to augment VLMs with mechanisms for serial processing of images, of the sort that enable human reasoners to manage the binding problem by selectively attending to individual objects one at a time [22, 33]. A number of methods have been proposed for sequential reasoning over images [12, 35], though none of these methods have yet been deployed at the scale of VLMs. An alternative approach (which may be unique to artificial systems) involves the use of slot-based methods for object-centric representation learning [4, 10, 16], which have been shown to dramatically improve performance in visual reasoning tasks without requiring sequential processing of images [8, 21], but which have also not been scaled to the level of current VLMs. It remains to be seen whether and how these techniques might contribute to improved reasoning in future VLMs, or whether new approaches will be needed to enable human-like visual reasoning.

6.1 Limitations & Future Directions

This study has several limitations that should be considered when interpreting the results. First, we limited our analysis to a relatively small set of tasks. The tasks in our study were selected to illustrate the different settings in which the binding problem may impact performance, while grounding our analysis in well known tasks from cognitive science that have been used to index such capacity constraints in humans. Future work may examine a broader set of tasks such as matrix reasoning tasks [3, 26, 39] that are more diagnostic of the reasoning failures arising due to issues with binding. Second, we primarily investigated propietary VLMs, for which we do not have detailed knowledge of their training data or architecture, or the ability to directly investigate their internal representations. We chose to focus on these models because they reflect the best-performing current set of VLMs (our experiments with the open-source Llava-1.5 yielded very poor performance on all tasks), but continued progress in the development of open-source VLMs should make it possible to investigate open-source models in future work. Finally, our work is focused particularly on characterizing the capacity constraints of VLMs arising due to issues with feature binding. While we propose a naive approach for improving performance by selectively processing sub-images independently, future work may explore more flexible methods for decomposing complex, multi-object reasoning tasks, especially by exploiting methods for object-centric representation learning [4, 5, 10, 16].

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E. Yiu, M. Qraitem, C. Wong, A. N. Majhi, Y. Bai, S. Ginosar, A. Gopnik, and K. Saenko. Kiva: Kid-inspired visual analogies for testing large multimodal models. arXiv preprint arXiv:2407.17773 , 2024.

C. Zhang, F. Gao, B. Jia, Y. Zhu, and S.-C. Zhu. Raven: A dataset for relational and analogical visual reasoning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5317-5327, 2019.

C. Zhang and S. Wang. Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data. arXiv preprint arXiv:2401.17600 , 2024.

A Appendix: Supplementary Figures

2D Results

3D Results

Visual Search

Numerosity Estimation

2D Results

3D Results

B Appendix: Prompts for Vision-Language Model Experiments

B.1 Numerical Estimation

1. 2D VLM Prompt

You are presented with an image containing several objects. Your task is to accurately count the number of objects in the image. Follow these

instructions carefully: 1. Begin by describing each object in the image. 2. Conclude your response by providing the total count of objects as an integer , enclosed in square brackets. Only the number should be enclosed in square brackets.

2. 3D VLM Prompt

You are presented with an image containing several shapes. Your task is to accurately count the number of shapes in the image. Follow these instructions

carefully: 1. Begin by describing each shape in the image. 2. Conclude your response by providing the total count of shapes as an integer , enclosed in square brackets. Only the number should be enclosed in square brackets.

3. T2I Prompt

Render an image with exactly {n} {object_name}, each distinctly separated and easily countable , arranged against a uniform background in photorealistic style.

B.2 Visual Search

1. 2D Disjunctive Search Prompt

You are presented with an image containing several shapes. Your task is to determine if there are any green shapes in the image. Follow these

steps carefully: 1. Describe each shape in the image , noting their color. 2. Conclude your response by stating [True] if there are any green shapes , or [ False] if there are none. Enclose your final answer in square brackets , as shown.

2. 3D Disjunctive Search Prompt

You are presented with an image containing several objects. Your task is to determine if there are any red objects in the image. Follow these steps

Conclude your response by stating [True] if there are any red objects , or [ False] if there are none. Enclose your final answer in square brackets , as

3. 2D Disjunctive Search (Variable Color) Prompt

Conclude your response by stating [True] if all shapes are the same color , or [False] if there is an "oddball" shape that is a different color. Enclose your final answer in square brackets , as shown.

4. 2D Conjunctive Search

You are presented with an image containing a set of letters , specifically the letters 'L' and 'T'. These letters will appear in either red or green. Your task is to determine if there are any green 'L's in the image. Follow

Conclude your response by stating [True] if the letter 'L' appears in or [False] if there are no green 'L's. Enclose your final answer in square brackets , as shown.

5. 3D Conjunctive Search Prompt

B.3 Scene Description

1. 2D VLM Prompt

2. 3D VLM Prompt

3. T2I Prompt

Render an image in photorealistic style with exactly {objects_string} arranged against a uniform background , each distinctly separated. Include only these objects in the image and nothing else.

B.4 Relational Match to Sample (RMTS)

1. Full Task - Unified Condition

2. Full Task - Decomposed Condition

3. Relation Decoding - Unified condition

4. Relation Decoding - Decomposed condition

5. Single Feature Decoding - Unified condition

6. Single Feature Decoding - Decomposed condition

7. All Feature Decoding - Unified condition

8. All Feature Decoding - Decomposed condition

C Appendix: Human Evaluations

(a) Binding task evaluation

(b) Counting task evaluation

Round1/50

For each object in the image, check the corresponding box. If there are multiple of the

same kind of object in the image, check off multiple corresponding boxes.

blueballoon green teacup

List any additional objects which were not in the list of check boxes, separated by a

comma. For example, if the image had three red circles but there were only two *red

circle* check boxes, please Iist ONE *red circle* below.

Submi

Round2/50

Createexuctyslyay7edinetes,andreaslyeasilycountb

0000000000

Count the number of ponies in the image. Please enter an integer value.

Submit

Human evaluations of the text-to-image variants of the counting and binding tasks were conducted on Prolific. Participants were asked to count the number of objects in each T2I counting task image, or asked to match objects to provided labels in each T2I scene description task image. They were also asked to list extraneous objects (generated objects not in the input prompt) when evaluating the binding images. Participants were paid a total estimated wage of approximately $12/hour, and total compensation for the entirety of the human evaluations was approximately $600. All participants provided informed consent. The study was approved by the Princeton University IRB.

D Appendix: Computational Resources

Experiments were conducted on closed source models, and therefore did not require any specialized hardware. Generation of the 3D variants of the task were more computationally intensive and were performed in parallel on a local cluster, with 16GB RAM allocated per CPU per job.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nVision Language Models (VLMs), including models that are able to describe images, such as GPT-4v, and models that generate images given a text description, such as DALL-E, display several failures in tasks that humans are able to perform effortlessly. Such tasks include counting, localizing a given object in an image, and visual analogy. This paper interprets these failures as resulting from a trade-off between representational flexibility (the ability to generalize compositionally to novel combinations of features) and channel capacity (the number of entities that can be represented simultaneously). This trade-off corresponds to a well-established phenomenon in cognitive science, the binding problem. The binding problem identifies a series of failures in human visual perception, that arise in many situations in which human subjects need to process multi-object scenes under time constraints. Such constraints prevent them from processing those scenes in a sequential way, and force them to rely on parallel processing, which incurs into capacity limits. This leads to phenomena such as illusory conjunctions, in which a feature is bound to the wrong object: for example, a green triangle might be falsely detected in a scene comprising red triangles, green circles and red circles. The failures shown by VLMs are remarkably similar, suggesting that they might result from the lack of a serial processing mechanism like the one humans can rely on in the absence of time constraints. To test this hypothesis, in this work VLMs are tested on two classic cognitive tasks (counting and visual search), showing error patterns similar to time-constrained human subjects. In counting, a sharp drop in performance is observed with scenes comprising more than 4 to 6 objects, a similar range to that beyond which humans are unable to “subitize” (reliably count objects in parallel, without serial processing). In visual search, conjunctive search, in which a unique combination of features needs to be detected, led to a sharp drop in performance with increasing numbers of objects, while disjunctive search, in which targets can be detected based on a single feature, did not. These results are remarkably similar to the symptoms of the binding problem in humans. The authors further posit that these failures result from interference between target and distractor objects, and not by the number of objects alone. They test this hypothesis by parametrically generating scenes that vary in their feature entropy: the amount of variation in the two features, shape and color, across all objects. Using a scene description task, they find that errors were most frequent when the feature entropy was intermediate, leading to a combination of diversity in features and sharing of features between objects. Finally, the VLMs are tested on a visual analogy task. The known failures of these models in solving these tasks is hypothesized to also depend on limitations in the number of objects that can be processed in parallel. To alleviate this problem, the authors propose a simple “decomposed condition”: feeding the target stimuli (pairs of objects) to the models in sequence, rather than as part of a single image (”unified condition”). The decomposed condition mimicks the serial processing that humans adopt in similar tasks. In both the analogy task and extraction of individual object features, VLMs perform better in the decomposed condition than the unified condition, providing further evidence that VLMs’ failures are due to capacity limits in binding features to multiple objects in parallel.\n\nSTRENGTHS:\n- The paper presents a simple framework to explain failure cases of VLMs, directly inspired by a long tradition of research in cognitive science. It is a great example of how cognitive science research can inform the study of widespread deep learning models, with many potential applications in evaluating and improving such models.\n- The research methods are sound: the link between the theoretical question and the experiments is clear, and models are tested across multiple tasks and conditions (e.g. 2D and 3D scenes, different object categories) to ensure that results are robust.\n- Creative, but at the same time simple and elegant, ways to measure the relation between the binding problem and VLM failures (feature entropy) and to alleviate the binding problem in VLMs (decomposed condition) are proposed.\n- The interpretation of the results is fair and clear.\n\nWEAKNESSES:\nThe weaknesses are mostly related to the lack of detail concerning some of the methods, or the lack of explanations about certain choices. No major shortcoming was found by this reviewer.\n\n- In some of the prompts used to run experiments on the models (e.g. for counting), the model is first prompted to describe every object in the image, and then to perform the task. In others (e.g. disjunctive visual search) the model is directly asked to answer the question of interest (e.g. whether a target object is present in the image or not). Moreover, prompts from some tasks (e.g. 2D conjunctive search) include a form of role-playing \"You have perfect vision and pay great attention to detail...\". Since it is well known that similar techniques can have a great impact on the quality of language models' responses, the authors should provide an explanation of why these techniques were used in certain conditions and not others. Otherwise, performance comparisons between conditions (e.g. conjunctive vs. disjunctive search) can become harder to interpret.\n- In the scene description task, a language model is used to parse the descriptions. These are, I suppose, harder to parse than the simple answers provided in other tasks, such as the number of objects in the counting task or True or False in the visual search task. The authors should show that the language model was accurate in parsing these descriptions. Manually validating all of them would be infeasible, but at least a few examples to show the language models' accuracy should be provided in the supplementary materials.\n- Also in the scene description task, the generation of scenes with different feature entropies should be explained in more detail. Did the feature entropy manipulation simply consist in setting a particular number of shapes and features, and assigning them to a fixed number of objects? Or was the feature entropy computed in a different way? Making this clear would also help interpreting the values on the feature entropy that are shown in Figure 3.\n- In Figure 3, it is a bit hard to tell how many levels of feature entropy each number of objects has. For example, the 10 objects condition has two small bends that make it look like there are only three levels. For visualization purposes, I would recommend adding markers to the plot. Additionally, the values on the x-axis don't seem to align with the actual levels of feature entropy that were used. I understand that different number of objects had different levels, making it impossible to accomodate all possible levels, but if at least some of them could be aligned that would make the plot easier to read.\n- Also related to the scene description task, an explanation should be added of how the \"edit distance between the true description of the scene and the model's description\", the measure of errors, was computed. Does this mean that, for each (ground-truth) object in the scene, the most similar object (in terms of shape and color) in the description was chosen, and differences between that object and the ground truth were counted (e.g. color different = 1 error, shape different = 1 error)? How was, for example, a missing object in the description counted? As 2 errors, as both the color and shape were missing? A detailed explanation should be added.\n- The visual analogy task is similarly missing a detailed explanation of the pair generation process. In particular, what were the relations included in the task? Figure 4 apparently shows a \"sameness\" relation, in which target pair 2 is more similar to the source pair as they both feature objects that differ in color. Was this the only relation included? Also, on page 8, line 272 you write that the incorrect target \"shared only one of the relations\" with the source pair. I assume that this means that in figure 4, for example, TP2 shares both the \"same shape\" and the \"different color\" relation with the source pair, while TP1 only shares \"same shape\". Making this explicit and clarifying with an example would make this part easier for the reader.\n- The decomposed condition was only applied to the analogy task: was there a reason for this? As it's proposed as a general recipe for alleviating feature binding problems in VLMs, it would be more convincing if it was shown to work on a variety of tasks. The other tasks in the paper, in particular, seem to me like they would be amenable to be presented sequentially to the models. For example, a sequence of images containing only a few items at a time could be presented in visual search, simulating the way in which human participants serially scan such displays with eye movements in visual search experiments. If I am missing some practical reasons why conducting these experiments with a decomposed condition would be infeasible, or uninformative, I would be grateful to the authors if they could explain them.\n\nQUESTIONS:\nNo particular questions. I have mentioned a few points that I believe should be clarified in the \"weaknesses\" section, as I believe the paper would benefit from the addition of their answers.\n\nLIMITATIONS:\nOne limitation which has not been acknowledged is the use of closed-source models. This could compromise the reproducibility of this work, and will limit the ability of follow-up works to investigate the mechanisms underlying these findings.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work investigates the performance of the vision language model (VLM) GPT-4v and text-to-image model DALL-E-3 on multi-object reasoning tasks. According to the literature, humans perform well on multi-object tasks because they learn a compositional representation of the world and use serial processing to dynamically combine object features, overcoming the binding problem. However, in time-constrained tasks, they fail to use serial processing and rely on parallel processing, leading to illusory conjunction. In this work, the authors hypothesize, without testing, that VLMs learn a compositional representation for the sake of generalization but are not compelled to develop a serial-processing mechanism. According to the authors, this leads to failure in multi-object reasoning tasks, thus exhibiting the same limitations as humans in time-constrained tasks. The authors test these limitations on four multi-object reasoning tasks: counting, visual search, scene description, and visual analogy. In these tasks, the authors show that the performance of VLMs decreases as the number of objects increases, supporting the conclusion that VLMs are not able to solve the binding problem because they lack the mechanism to perform serial processing.\n\nSTRENGTHS:\n- The paper is straightforward and easy to understand. The authors did a good job making it accessible, and the presentation and sectioning help in grasping the content.\n- This work seeks to understand the limitations of VLMs through the lens of human cognition, which could provide valuable insights. \n- Originality in the problem statement: In terms of performance, VLMs in their normal regime are equivalent to humans in time-limited tasks.\n- The investigation utilizes various types of experiments, each guided by studies conducted on humans, and they look easy to redo and the empirical results seem relevant.\n\nWEAKNESSES:\nMy score would increase if the following weaknesses were addressed:\n- The hypothesis that VLMs learn a compositional representation for generalization purposes could benefit from a reference if possible, since the discussion depends on that hypothesis.\n- Despite conducting various types of experiments (four in total), each individual experiment lacks sufficient variability. For instance, in the counting experiment, although they vary the colors of the circles, they do not vary their sizes or shapes. Another example, for the poppout task it would have been nice to also alternate the color of target and distractors.\n- The authors carried out the experiment on a very small group of language models (two).\n\nSome typos: \n- Figure 1, top right: the x-axis should be integers in the plot of GPT-4v counting performance\n- Line 156, 188: should be ‘Figure 2’ and not ‘Figure 1’\n\nQUESTIONS:\nIf we carefully engineer prompts for VLMs to sequentially focus and analyze each object in an image, considering they can learn a compositional representation as claimed, thus they should theoretically possess all the elements necessary to solve the binding problem? Can you elaborate on that?\n\nLIMITATIONS:\nSee “Weaknesses”", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n**Summary:**\n\tThe paper investigates the counting abilities of VLMs. It puts forward a cognitive science inspired perspective, where VLMs are fundamentally constrained in their processing of multi-object scenes due to a lack of serial processing mechanisms.\n\n**Decision:**\n\tThe paper is well structured and investigates an interesting (albeit a bit constrained) question concerning VLMs object reasoning abilities. While some of the conclusions seem overstated to me, I nonetheless recommend accept, since the authors present novel findings that improve the research community's understanding of VLMs visual reasoning abilities.\n\nSTRENGTHS:\n*Originality*\n\t\tThe underlying question is timely and well motivated. The cognitive science derived approach is principled and the authors perform a number of interesting experiments to further delineate the issues underlying counting in VLMs.\n\n*Quality*\n\t\tThe submission is technically sound. I don't agree with all resulting claims, but the methods are appropriate.\n\n*Clarity*\n\t\tThe submission is clearly written and follows a good outline.\n\n*Significance*\n\t\tThe paper yields new data which allows for unique conclusions, however the scope of the investigation is somewhat limited.\n\nWEAKNESSES:\nI have brought it up in the questions sections.\n\nQUESTIONS:\n- Figure 1: There is a spike at 9 objects for both models. Do you have an idea on why this might be the case? [1] (which you already cite) also find the same spike and offer some potential explanations for this (see Figure 5).\n- Answer parsing is again conducted by a large language model. I am wondering how accurate this parsing is. Did you compute any metrics to check the accuracy of the final parsed output or run some qualitative checks?\n-  In line 189 you write \"GPT-4v demonstrates human-like capacity constraints in its ability to perform visual search in multi-object settings.\" I was wondering what human level performance is in this task? It would be nice to have some more information to compare the model performance to. The same is true for line 127, where I'm not sure this is a fair comparison. Sure, the number of objects the models can count well are somewhat similar to the subitizing limit of humans, but this task should not require rapid estimation of the number of objects and therefore we would expect humans to be able to count well above 6 objects here anyways.\n- The sentence in line 303 reads a bit odd, it should probably read \"facilitate generalization\" instead of facilitation and there might be a comma missing before?\n- If I understand correctly you describe a different measure for the model performance for the last task in Section 4.2, would it not be possible to again report the proportion of correct answers here? I find it hard to compare this experiment with the previous experiments.\n- To me the take aways seem overstated. What does lack of serial processing for VLMs mean? I don't think VLMs are fundamentally unable to sequentially process an image and therefore they should in theory be able to \"selectively attend to individual objects one at a time\". This is also why I don't completely follow the conclusion that the VLMs show \"human-like capacity constraints\". I would argue they should be able to do sequential processing and should therefore not be measured against the human subitizing limit anyways. Maybe you can offer some more details on this argument.\n\n[1] Rane, S., Ku, A., Baldridge, J., Tenney, I., Griffiths, T., & Kim, B. (2024). Can Generative Multimodal Models Count to Ten? Proceedings of the Annual Meeting of the Cognitive Science Society, 46. Retrieved from https://escholarship.org/uc/item/8kz5787g\n\nLIMITATIONS:\nThe paper is well structured and investigates an interesting (albeit a bit constrained) question concerning VLMs object reasoning abilities. While some of the conclusions seem overstated to me, I nonetheless recommend accept, since the authors present novel findings that improve the research community's understanding of VLMs visual reasoning abilities.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper puts forth the hypothesis that VLMs suffer from similar constraints as humans in multi-object visual processing, especially\nwith respect to \"binding\".  Results from cognitive science show human constraints in numerical estimation, visual search, and relation\nidentification when not able to do serial processing, which is required for \"binding\" different object features together. The paper hypothesizes that lack of serial processing and binding in VLMs is responsible for failures in these tasks.  The paper gives experimental\nresults that support this hypothesis.\n\nSTRENGTHS:\nThis is a good paper -- it gives a cognitive-science-based approach to understanding VLM behavior. It is well-written and has a mostly clear discussion of hypotheses, experiments, and results.\n\nWEAKNESSES:\nThe paper leaves out some important information.  For each task, the authors prompted GPT-4v to describe the input images, but as far as I can tell, they did not give any evaluation of how well GPT-4v was able to describe them.  Other work (e.g., http://www.arxiv.org/abs/2407.06581) has shown that GPT-4v has trouble describing such synthetic images and often will hallucinate.\n\nHere, the authors should include the results on how well GPT-4v can even parse what is in the images (including shapes, colors, etc.) and the authors should assess whether that failure to describe simple images is related to its failure on these tasks.  To what extent are the failures a matter of lack of binding features, or of missing or hallucinating features?\n\nA couple of typos/corrections:\n\nIn Section 3, \"Figure 1\" should be \"Figure 2\".\n\nIn the caption for Figure 2, \"blue column\" should be \"red column\" and \"red column\" should be \"blue column\".\n\nQUESTIONS:\nFigure 2 caption: \"Performance for 2D and 3D task variants\" -- performance of what?  GPT-4v?  I also suggest in that caption that you\nshould say what the targets are for each search (red circle, red \"L\", red sphere, red sphere).\n\nIn Section 3.1., explain what the task is for GPT-4V: \"Is there a red circle?\" \"Is there a red L?  This is explained in the appendix (A.2) but should be explained here in the main paper.\n\nLooking at the visual search task as described in A.2, it seems that the task wasn't actually \"search\" in the sense that, unlike humans, GPT-4V doesn't have to localize the target object.  In these kinds of tasks, aren't humans asked to localize the target?  How does this affect the comparability of the results between GPT-4V and humans on this task?\n\nIn Section 3.1, explain why in half the images, targets were left out. This doesn't make sense unless readers know what the specific task is.\n\nIn Section 3.1 you say \"VLMs demonstrate similar capacity constraints to humans.\"  What do you mean by \"similar\"?  Do humans show a similar success/failure curve as those in Figure 2? Or is the similarity just that humans perform well on disjunctive search and badly on\nconjunctive search (if not enough time for serial processing)?\n\nIn Section 3.2, explain how the \"accuracy\" of the model is calculated.\n\nIn Section 4.2 you refer to \"the true description of the scene\". Where did this \"true description\" come from?  Also, define \"edit\ndistance\".  I was also confused because each scene could have different correct descriptions with different levels of detail.  Was it possible that the VLM gave one version of a correct description but it was counted as wrong because it included details that the \"true description\" didn't contain?\n\nI was also confused by the visual analogy task in Figure 4.  It seems to me that both Target Pair 1 and Target Pair 2 could be considered\nthe correct answer.  The source pair has two obvious relations: \"same star shape\" and \"different color\".  The former would map to Target 1 and the latter would map to Target 2.  Which Target did you consider correct, and why?\n\nLIMITATIONS:\nThe authors do a good job of discussing limitations.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-14"}