HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics

2024-08-30

AI Summary

Long video understanding faces fundamental challenges including modeling long-range temporal dependencies, handling abundant redundant information, and extracting high-level semantic concepts. Moving beyond the conventional paradigm that treats long videos as extended short clips, this work draws inspiration from human episodic memory to propose a novel framework jointly modeling action temporal structure and global semantics. Our key contributions are: (1) an Episodic Compressor (ECO) for hierarchical, multi-granularity temporal representation aggregation; (2) a Semantic Transformer Retriever (SeTR) enabling efficient long-range dependency modeling and high-order semantic concept extraction via low-dimensional features; and (3) a micro–semi–macro cross-scale representation fusion mechanism. Built upon a lightweight, hierarchical Transformer-based compression architecture with context-aware semantic retrieval, our method achieves state-of-the-art performance under both zero-shot and fully supervised settings across multiple long-video understanding benchmarks, significantly outperforming existing approaches.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Existing research often treats long-form videos as extended short videos, leading to several limitations: inadequate capture of long-range dependencies, inefficient processing of redundant information, and failure to extract high-level semantic concepts. To address these issues, we propose a novel approach that more accurately reflects human cognition. This paper introduces HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics, a model that simulates episodic memory accumulation to capture action sequences and reinforces them with semantic knowledge dispersed throughout the video. Our work makes two key contributions: First, we develop an Episodic COmpressor (ECO) that efficiently aggregates crucial representations from micro to semi-macro levels, overcoming the challenge of long-range dependencies. Second, we propose a Semantics ReTRiever (SeTR) that enhances these aggregated representations with semantic information by focusing on the broader context, dramatically reducing feature dimensionality while preserving relevant macro-level information. This addresses the issues of redundancy and lack of high-level concept extraction. Extensive experiments demonstrate that HERMES achieves state-of-the-art performance across multiple long-video understanding benchmarks in both zero-shot and fully-supervised settings.

Problem

• Capturing long-range dependencies in long-form videos
• Efficiently processing redundant video information
• Extracting high-level semantic concepts from videos

Innovation

• Episodic COmpressor aggregates multi-level representations efficiently
• Semantics ReTRiever enriches features with broad context
• Modules reduce latency and memory usage significantly