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Found 9 papers, 6 papers with code
Semantic Equitable Clustering: A Simple, Fast and Effective Strategy for Vision Transformer
The Vision Transformer (ViT) has gained prominence for its superior relational modeling prowess.
Vision Transformer with Sparse Scan Prior
In recent years, Transformers have achieved remarkable progress in computer vision tasks.
Band-Attention Modulated RetNet for Face Forgery Detection
The transformer networks are extensively utilized in face forgery detection due to their scalability across large datasets. Despite their success, transformers face challenges in balancing the capture of global context, which is crucial for unveiling forgery clues, with computational complexity. To mitigate this issue, we introduce Band-Attention modulated RetNet (BAR-Net), a lightweight network designed to efficiently process extensive visual contexts while avoiding catastrophic forgetting. Our approach empowers the target token to perceive global information by assigning differential attention levels to tokens at varying distances.
ViTAR: Vision Transformer with Any Resolution
Firstly, we propose a novel module for dynamic resolution adjustment, designed with a single Transformer block, specifically to achieve highly efficient incremental token integration.
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text generation task.
RMT: Retentive Networks Meet Vision Transformers
To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes.
Lightweight Vision Transformer with Bidirectional Interaction
This paper proposes a Fully Adaptive Self-Attention (FASA) mechanism for vision transformer to model the local and global information as well as the bidirectional interaction between them in context-aware ways.
Rethinking Local Perception in Lightweight Vision Transformer
The combination of the AttnConv and vanilla attention which uses pooling to reduce FLOPs in CloFormer enables the model to perceive high-frequency and low-frequency information.
Ranked #579 on
Image Classification
on ImageNet
