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Context-Aware Temporal Embedding of Objects in Video Data

Ahnaf Farhan, M. Shahriar Hossain

TL;DR

This work addresses the limitation of appearance-only object representations in video by introducing context-aware temporal embeddings that model how object context evolves over time. It defines static and temporal embeddings $E_{static}$ and $E_{temporal}$ with sizes $|O| imes|e|$ and $|O| imes|T| imes|e|$, respectively, learned via diffusion-based contextual discrepancy scores across frames and timestamps. The approach combines context windows (frame, surrounding frames, and neighboring timestamps), frequency-driven diffusion, negative sampling, and a neural network to produce robust embeddings, which can be fused with visual features for downstream tasks and enable video narration with LLMs. Experimental results on synthetic and real datasets show improved clustering, contextual classification, and narrative capabilities, illustrating the practical impact for video understanding and surveillance analytics.

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.

Context-Aware Temporal Embedding of Objects in Video Data

TL;DR

This work addresses the limitation of appearance-only object representations in video by introducing context-aware temporal embeddings that model how object context evolves over time. It defines static and temporal embeddings and with sizes and , respectively, learned via diffusion-based contextual discrepancy scores across frames and timestamps. The approach combines context windows (frame, surrounding frames, and neighboring timestamps), frequency-driven diffusion, negative sampling, and a neural network to produce robust embeddings, which can be fused with visual features for downstream tasks and enable video narration with LLMs. Experimental results on synthetic and real datasets show improved clustering, contextual classification, and narrative capabilities, illustrating the practical impact for video understanding and surveillance analytics.

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.
Paper Structure (36 sections, 24 equations, 25 figures, 6 tables)

This paper contains 36 sections, 24 equations, 25 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Text Embeddings
  4. Image Embeddings
  5. Video Embeddings
  6. Contextual Embeddings
  7. Temporal Embeddings
  8. Temporal Object Embedding
  9. Problem Description
  10. Methodology
  11. Frame Extraction
  12. Detection and Selection of Context Objects
  13. Training Data Preparation: Contextual Discrepancy Score between a Reference Object and a Context Object
  14. Diffusion-based contextual discrepancy score for static embedding
  15. Diffusion-based contextual discrepancy score for temporal embedding
  16. ...and 21 more sections

Figures (25)

  • Figure 1: Difference between visual similarity and contextual similarity.
  • Figure 2: Context changes over time.
  • Figure 3: Complete pipeline of embedding-generation from a video.
  • Figure 6: Neural network model to generate the temporal visual object embeddings.
  • Figure 7: Fusion of visual features (using either CNN or ResNet50) and temporal contextual embeddings.
  • ...and 20 more figures