Do Pre-trained Vision-Language Models Encode Object States? (2409.10488v1)

Abstract: For a vision-LLM (VLM) to understand the physical world, such as cause and effect, a first step is to capture the temporal dynamics of the visual world, for example how the physical states of objects evolve over time (e.g. a whole apple into a sliced apple). Our paper aims to investigate if VLMs pre-trained on web-scale data learn to encode object states, which can be extracted with zero-shot text prompts. We curate an object state recognition dataset ChangeIt-Frames, and evaluate nine open-source VLMs, including models trained with contrastive and generative objectives. We observe that while these state-of-the-art vision-LLMs can reliably perform object recognition, they consistently fail to accurately distinguish the objects' physical states. Through extensive experiments, we identify three areas for improvements for VLMs to better encode object states, namely the quality of object localization, the architecture to bind concepts to objects, and the objective to learn discriminative visual and language encoders on object states. Data and code are released.

• The paper reveals that pre-trained Vision-Language Models (VLMs) perform significantly worse at recognizing object states compared to object recognition, showing nearly a 30% accuracy drop on the ChangeIt-Frames dataset.
• Current VLMs struggle due to insufficient object localization and weak binding of visual features to object states, treating images as concepts rather than discrete objects with properties.
• Future work should focus on architectural changes, object-centric representations, and incorporating temporally rich or physically grounded datasets to improve VLM understanding of object states.

Insights into Object State Encoding by Pre-trained Vision-LLMs

The paper entitled "Do Pre-trained Vision-LLMs Encode Object States?" by Newman et al. conducts a comprehensive examination of the capability of Vision-LLMs (VLMs) to encode object states using the curated ChangeIt-Frames dataset. This investigation is pivotal for understanding whether current VLMs, trained on large-scale web data, inherently possess the ability to capture and distinguish various object states, such as those pertinent to physical transformations (e.g., a whole apple versus a sliced apple), which are critical for tasks requiring physical commonsense reasoning.

Key Findings and Methodology

The paper evaluates nine state-of-the-art open-source VLMs, including models utilizing dual-tower contrastive learning approaches and Multimodal LLMs (MLLMs) with generative backbones. The authors introduce ChangeIt-Frames, a meticulously curated dataset derived from the ChangeIt video dataset, which comprises images representing diverse object states verified through human annotations. The evaluation focuses on the models' performance in zero-shot classification tasks, assessing their object recognition and state discrimination capabilities through a selection of representative prompts.

Significantly, while VLMs typically demonstrate impressive object recognition skills, their ability to correctly discern object states is substantially weaker. The paper underscores a consistent accuracy drop of nearly 30% in recognizing object states compared to object recognition.

Limitations and Implications

Despite efforts such as fine-tuning PhysVLM with physically grounded data, improvements in encoding object states remain inadequate. The paper identifies insufficient object localization and a deficiency in discriminative visual-language encodings as primary limitations. The findings suggest that existing models might treat images as collections of concepts rather than discrete objects, thereby failing to adequately bind visual features to object states.

The examinations further reveal that simply scaling model size or using extensive datasets does not rectify these shortcomings sufficiently. Both standard and distractor prompt evaluations exhibit model vulnerabilities to subtle semantic disparities, indicating an avenue where future research could focus on enhancing the robustness and precision of VLM text encoders at the phrase level.

Recommendations and Future Research Directions

The authors propose a need for specific architectural modifications and new pre-training objectives that incorporate object-centric representations and localization for better state recognition. The recommendation extends to incorporating tracking mechanisms in VLMs for better modeling of state transitions, particularly through the utilization of temporally rich datasets, such as video data.

Future work may also investigate leveraging Multimodal LLMs with enhanced language modeling capabilities to mitigate challenges observed with distractor prompts. Importantly, research could explore developing more physically grounded VLM training datasets, facilitating an inductive bias towards object-centric learning and improving visual feature binding to object states.

Conclusion

This paper provides a pertinent analysis of the capabilities and limitations of current Vision-LLMs in capturing object states, revealing significant gaps that must be addressed to advance physical commonsense reasoning in AI systems. By documenting these findings and suggesting paths forward, the research promotes understanding of the intricacies in model training and architecture essential for encoding the physical state's information comprehensively. Overall, this paper sets the stage for the development of subsequent VLM generations capable of nuanced understanding and interaction within the physical world—a critical aspect of achieving more sophisticated AI systems.