Context-Aware Temporal Embedding of Objects in Video Data

TL;DR

This paper introduces a novel context-aware temporal embedding method for objects in videos, leveraging object relationships over time to improve recognition and enable video narration, surpassing traditional appearance-based approaches.

Contribution

The paper presents a new temporal embedding model that incorporates contextual object relationships, enhancing video analysis beyond visual appearance alone.

Findings

Embeddings improve object classification accuracy.

Enhanced video narration capabilities demonstrated.

Outperforms traditional appearance-based methods.

Abstract

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.