CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding (2311.03354v1)

Abstract: A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundation models (VLMs) fall short of such compositional abilities due to their "bag-of-words" behaviors and inability to construct words that correctly represent visual entities and the relations among the entities. To this end, we propose CoVLM, which can guide the LLM to explicitly compose visual entities and relationships among the text and dynamically communicate with the vision encoder and detection network to achieve vision-language communicative decoding. Specifically, we first devise a set of novel communication tokens for the LLM, for dynamic communication between the visual detection system and the language system. A communication token is generated by the LLM following a visual entity or a relation, to inform the detection network to propose regions that are relevant to the sentence generated so far. The proposed regions-of-interests (ROIs) are then fed back into the LLM for better language generation contingent on the relevant regions. The LLM is thus able to compose the visual entities and relationships through the communication tokens. The vision-to-language and language-to-vision communication are iteratively performed until the entire sentence is generated. Our framework seamlessly bridges the gap between visual perception and LLMs and outperforms previous VLMs by a large margin on compositional reasoning benchmarks (e.g., ~20% in HICO-DET mAP, ~14% in Cola top-1 accuracy, and ~3% on ARO top-1 accuracy). We also achieve state-of-the-art performances on traditional vision-language tasks such as referring expression comprehension and visual question answering.

Composing Visual Entities and Relationships in LLMs: An Overview of CoVLM

The paper introduces CoVLM, a novel framework aimed at enhancing the compositional reasoning capabilities of LLMs through dynamic interaction with visual encoders and detection networks. CoVLM addresses the current limitations of Vision-LLMs (VLMs), which typically lack the ability to effectively compose visual entities and their relationships due to their reliance on holistic image embeddings and "bag-of-words" behaviors. The approach taken in this research involves the introduction of communicative decoding techniques that enable more sophisticated visual-text interactions.

Methodology and Implementation

At the core of CoVLM is a dynamic communication system implemented via specially designed tokens that facilitate iterative vision-to-language and language-to-vision exchanges. The framework employs a Vision-LLM architecture that integrates a visual detection system and an LLM, with the latter being guided to generate language descriptions contingent on visual cues.

1. Visual Module Composition: The visual module comprises a CLIP-based image encoder and a YOLOX-inspired detection network. This setup allows for the extraction and proposal of relevant regions in an image, which are processed to generate bounding boxes and corresponding feature embeddings.
2. LLM Adaptation: The paper utilizes pre-trained Pythia models as the LLM backbone, augmenting them with novel communication tokens. These tokens mediate the flow of information between language processing and visual inputs, ensuring compositional integrity across generated expressions.
3. Iterative Communication: CoVLM leverages top-down and bottom-up communicative processes. Top-down communication involves directing the detection network using LLM-generated information, while bottom-up communication feeds detected visual features back into the LLM to influence further language generation.

Experimental Results

The performance of CoVLM is evaluated across several compositional reasoning benchmarks, demonstrating substantial improvements over existing VLMs:

• HICO-DET: CoVLM improves mean average precision (mAP) by about 20%, demonstrating its superior capability in human-object interaction detection compared to prior models.
• Cola and ARO: The model achieves a marked increase in top-1 accuracy by approximately 14% and 3% respectively, showcasing its enhanced ability to handle tasks involving relational compositionality.
• Standard Vision-Language Tasks: CoVLM also shows competitive performance in task areas such as referring expression comprehension and visual question answering.

Theoretical and Practical Implications

The theoretical advancements presented by CoVLM lie in its robust integration of visual and linguistic cues, setting a precedent for future developments in multimodal AI systems. Practically, CoVLM's superior performance in tasks that require detailed relational understanding and reasoning paves the way for its application across domains where precise and context-aware interpretation of visual information is crucial.

Future Directions

Despite the promising results, the paper notes the limited exploration of object-attribute and spatial event compositionality, suggesting these areas as prospective research directions. Further refinement of communicative tokens to better encapsulate these aspects could lead to more comprehensive models that fully emulate human-like compositional reasoning.

In conclusion, CoVLM represents a significant stride in the field of AI, particularly in the seamless convergence of visual and linguistic processing. Its methodological innovations offer a template for building future models capable of more nuanced and contextually rich understanding, reflecting the complexity inherent in human cognition. As the landscape of AI continues to evolve, approaches like those detailed in this paper will undoubtedly play a pivotal role in bridging existing gaps between different modal interpretations.