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Found 6 papers, 6 papers with code
Efficient Transfer Learning for Video-language Foundation Models
Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks.
Stochastic Layer-Wise Shuffle: A Good Practice to Improve Vision Mamba Training
Recent Vision Mamba models not only have much lower complexity for processing higher resolution images and longer videos but also the competitive performance with Vision Transformers (ViTs).
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
We believe that the GenVideo dataset and the DeMamba module will significantly advance the field of AI-generated video detection.
Conditional Prototype Rectification Prompt Learning
Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the scenario of limited data, introducing only a few parameters to harness task-specific insights from VLMs.
Boosting Audio-visual Zero-shot Learning with Large Language Models
Recent methods mainly focus on learning multi-modal features aligned with class names to enhance the generalization ability to unseen categories.
Ranked #1 on
GZSL Video Classification
on ActivityNet-GZSL (cls)
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
