Look, Remember and Reason: Grounded reasoning in videos with language models (2306.17778v3)

Abstract: Multi-modal LLMs (LM) have recently shown promising performance in high-level reasoning tasks on videos. However, existing methods still fall short in tasks like causal or compositional spatiotemporal reasoning over actions, in which model predictions need to be grounded in fine-grained low-level details, such as object motions and object interactions. In this work, we propose training an LM end-to-end on low-level surrogate tasks, including object detection, re-identification, and tracking, to endow the model with the required low-level visual capabilities. We show that a two-stream video encoder with spatiotemporal attention is effective at capturing the required static and motion-based cues in the video. By leveraging the LM's ability to perform the low-level surrogate tasks, we can cast reasoning in videos as the three-step process of Look, Remember, Reason wherein visual information is extracted using low-level visual skills step-by-step and then integrated to arrive at a final answer. We demonstrate the effectiveness of our framework on diverse visual reasoning tasks from the ACRE, CATER, Something-Else and STAR datasets. Our approach is trainable end-to-end and surpasses state-of-the-art task-specific methods across these tasks by a large margin.

• The paper proposes the LRR framework that integrates low-level surrogate tasks and language model enhancements to improve video reasoning.
• It employs a two-stream video encoder and cross-attention layers to effectively capture both static scene details and dynamic object motions.
• Empirical results show significant improvements on benchmarks like ACRE, CATER, and Something-Else, setting new state-of-the-art performance levels.

Insightful Overview of "Look, Remember and Reason: Grounded reasoning in videos with LLMs"

The paper "Look, Remember and Reason: Grounded reasoning in videos with LLMs" presents an innovative methodology for enhancing the reasoning capabilities of multi-modal LLMs (LMs) when dealing with video inputs. It addresses the challenges of causal and spatiotemporal reasoning by proposing an approach that is fundamentally grounded in low-level visual detail extraction, making it a significant contribution to the area of machine reasoning with heterogeneous sensory inputs.

Key Methodological Advances

The central premise of the paper is the introduction of the Look, Remember, and Reason (LRR) framework. This approach emphasizes an intricate three-step process: looking at the visual scene to extract relevant low-level information, remembering by maintaining these details within the model's working memory, and reasoning to synthesize a response through high-level cognitive processing. Each of these steps is facilitated by novel components within the LRR architecture:

• Low-level Surrogate Tasks: The authors propose training LMs on low-level surrogate tasks such as object recognition, re-identification, and tracking. These tasks endow the model with the ability to ground its reasoning processes in fine-grained visual cues, crucial for understanding object motion and interactions in video data.
• Two-Stream Video Encoder: Utilizing spatiotemporal attention mechanisms, this component effectively captures both static and dynamic features of the video frames. It enables the model to discern scene structure and object motion, addressing the density and complexity inherent in video data.
• Cross Attention Layers in LM: By embedding cross attention layers between self-attention layers, the LRR model leverages the LLM's global semantic understanding to refine the extraction of low-level visual information. This top-down cross-attention facilitates the integration of visual information into the reasoning process.

Strong Numerical Results

The paper demonstrates impressive numerical results across various benchmarks, illustrating the effectiveness of the LRR framework. Specifically, the model significantly outperforms state-of-the-art approaches on datasets like ACRE, CATER, Something-Else, and STAR. On the ACRE dataset, the LRR model achieved an accuracy of 98.2% on the compositional split and 99.2% on the systematic split, surpassing other methods by a wide margin. Similarly, it exceeds previous results on the challenging compositional split of the Something-Else dataset and is competitive in object tracking tasks required by the CATER and STAR datasets. These results attest to the model's flexibility and its robust reasoning capability in diverse scenarios.

Implications and Future Directions

The practical implications of this research are considerable, given the increasing demand for AI systems that can interpret and reason about complex video data in fields such as autonomous vehicles, surveillance, and interactive AI systems. The capability to ground reasoning in low-level visual information while leveraging high-level LLM insights extends the functional reach of AI, potentially leading to more intuitive and context-aware digital assistants.

Theoretically, the approach outlined in this paper suggests new avenues for advancing multi-modal LMs, particularly by enhancing their capacity to process spatiotemporal information. Future work could explore the scalability of this framework with larger models or its application to other types of multi-modal data beyond video. Additionally, investigations into optimizing surrogate task selection and the incorporation of additional modalities could further refine model performance.

Overall, "Look, Remember and Reason: Grounded reasoning in videos with LLMs" enriches the field of multi-modal learning and lays the groundwork for more sophisticated AI capabilities in understanding dynamic video environments.