Context-Aware Temporal Embedding of Objects in Video Data
This work addresses the challenge of recognizing object interactions and event patterns in video data for applications like video analysis and object classification, representing an incremental improvement over traditional visual-only methods.
The paper tackled the problem of understanding temporal context in video analysis by proposing a model that constructs context-aware temporal object embeddings using adjacency and semantic similarities between objects from neighboring frames, enhancing downstream applications and enabling video narration with LLMs.
In video analysis, understanding the temporal context is crucial for recognizing object interactions, event patterns, and contextual changes over time. The proposed model leverages adjacency and semantic similarities between objects from neighboring video frames to construct context-aware temporal object embeddings. Unlike traditional methods that rely solely on visual appearance, our temporal embedding model considers the contextual relationships between objects, creating a meaningful embedding space where temporally connected object's vectors are positioned in proximity. Empirical studies demonstrate that our context-aware temporal embeddings can be used in conjunction with conventional visual embeddings to enhance the effectiveness of downstream applications. Moreover, the embeddings can be used to narrate a video using a Large Language Model (LLM). This paper describes the intricate details of the proposed objective function to generate context-aware temporal object embeddings for video data and showcases the potential applications of the generated embeddings in video analysis and object classification tasks.