Enhancing Video Transformers for Action Understanding with VLM-aided Training

Concetti Chiave

Integrating Visual Language Models with Vision Transformers enhances video action understanding by aligning spatio-temporal representations.

Sintesi

The content introduces the Four-Tiered Prompts (FTP) framework that combines Vision Transformers (ViTs) and Visual Language Models (VLMs) to improve video action understanding. The FTP framework focuses on different aspects of videos, such as action category, components, description, and context information. By aligning ViTs' visual encodings with VLM outputs during training, richer representations are generated, leading to state-of-the-art performance across various datasets. The integration process involves feature processors and classification layers that enhance the generalization ability of ViTs.
Structure:

Introduction to Video Action Understanding
Role of Vision Transformers in Spatio-Temporal Representation Learning
Limitations of ViTs in Generalization Across Datasets
Introduction of Visual Language Models for Improved Generalization
Proposal of the Four-Tiered Prompts (FTP) Framework
Detailed Explanation of the FTP Architecture and Training Process
Experimental Results on Various Datasets: Kinetics-400/600, Something-Something V2, UCF-101, HMDB51, AVA V2.2
Ablation Study on the Influence of VLMs, ViTs, and Prompt Combinations
Conclusion and Future Directions

Statistiche

We achieve remarkable top-1 accuracy of 93.8% on Kinetics-400.
Achieved top-1 accuracy of 83.4% on Something-Something V2.
Our approach consistently surpasses state-of-the-art methods by clear margins.

Citazioni

"In this paper, we propose the Four-tiered Prompts (FTP) framework that takes advantage of the complementary strengths of ViTs and VLMs."
"Our approach consistently yields state-of-the-art performance."
"By integrating the outputs of these feature processors, the ViT’s generalization ability can be significantly improved."

Approfondimenti chiave tratti da

Enhancing Video Transformers for Action Understanding with VLM-aided Training

by Hui Lu,Hu Ji... alle arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16128.pdf

Domande più approfondite

How can the FTP framework be adapted for other types of video analysis beyond action understanding?

The FTP framework can be adapted for various other types of video analysis by modifying the prompts and feature processors to focus on different aspects relevant to the specific task at hand. For instance, in tasks like object detection or scene segmentation, prompts could be designed to capture details about objects present in the video frames or contextual information about the scenes. By aligning these textual descriptions with visual encodings from ViTs, richer representations can be generated that cater to the requirements of diverse video analysis tasks.

What potential challenges could arise from over-reliance on Visual Language Models in video processing tasks?

Over-reliance on Visual Language Models (VLMs) in video processing tasks may introduce several challenges. One major challenge is related to computational complexity during inference, as VLMs typically require significant resources compared to Vision Transformers (ViTs). This could lead to slower processing times and higher costs. Additionally, VLMs may not always provide accurate or relevant textual descriptions for all types of videos, leading to potential errors in alignment with visual encodings. Moreover, if VLMs are not trained on a diverse range of data sources, they might exhibit biases that impact the quality and generalization ability of their outputs.

How might incorporating additional prompts or modifying existing ones impact the performance and flexibility of the FTP framework?

Incorporating additional prompts or modifying existing ones within the FTP framework can have a significant impact on its performance and flexibility. By introducing new prompts that capture different aspects of action understanding or adjusting existing prompts based on specific domain requirements, it allows for more tailored feature extraction and alignment between text embeddings and visual encodings. This enhanced alignment can improve model accuracy across various datasets while also increasing adaptability to different domains without requiring extensive retraining. However, adding too many prompts may increase computational overhead during training and inference unless carefully managed through efficient design strategies such as selective prompt usage based on task relevance.