Concetti Chiave
Efficiently process long video sequences using a text-conditioned resampler for improved performance in various tasks.
Sintesi
The content introduces the Text-Conditioned Resampler (TCR) module, designed to process long video sequences efficiently by localizing relevant visual features based on text conditions. The TCR bridges pre-trained visual and language models, enabling processing of over 100 frames at a time. The paper outlines the architecture, training methods, and empirical validation on tasks like NextQA, EgoSchema, and EGO4D-LTA challenge.
Introduction
Visual-language models have advanced significantly.
Models reasoning about object relationships through natural language are beneficial for various video applications.
Text-Conditioned Resampler (TCR)
TCR bridges pre-trained models via visual-to-language adapter modules.
Advantages include smaller memory footprint and leveraging large visual backbones without overfitting.
Model Details
TCR processes video frames with a transformer-based architecture conditioned on tasks.
Interaction of query sequence with visual features is through cross-attention only.
Experiments
Evaluation on datasets like Kinetics400, MSR-VTT, NextQA, EgoSchema, and EGO4D challenges.
Performance analysis based on the number of frames processed by the model.
Further Training Details
Pre-training stages involve captioning, temporal grounding, and denoising tasks.
Fine-tuning procedures vary for different downstream datasets.
Ablation Studies
Impact of conditioning prompts on model performance.
Importance of the number of frames processed by the model.
Optimal number of queries observed by the LLM for improved performance.
Conclusion
The TCR module offers an efficient solution for processing long video sequences with improved performance across various tasks.
Statistiche
TCR can process more than 100 frames at a time efficiently without optimized implementations.
Citazioni
"In this paper we present a Text-Conditioned Resampler (TCR), an architecture and pre-training method that tackles all of the challenges mentioned above."
"Models capable of perceiving long video sequences such as TCR will open up a promising new direction in research."