activity
20162026
most citedLocalViT: Analyzing Locality in Vision Transformers

285 citations · 838 across the 56 of their papers we have counts for

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Showing 2024Show all

10 papers · 1 filter

eess.IV2024★ 3 cited

MambaIRv2: Attentive State Space Restoration

Hang Guo, Yong Guo, Yaohua Zha +5

The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causa…

cs.CV2024

IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models

Hang Guo, Yawei Li, Tao Dai +2

Fine-tuning pre-trained diffusion models under limited budgets has gained great success. In particular, the recent advances that directly fine-tune the quantized weights using Low-…

cs.CV2024★ 1 cited

When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation

Yuli Zhou, Guolei Sun, Yawei Li +3

This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involve…

cs.CV2024

Unified Embedding Alignment for Open-Vocabulary Video Instance Segmentation

Hao Fang, Peng Wu, Yawei Li +2

Open-Vocabulary Video Instance Segmentation (VIS) is attracting increasing attention due to its ability to segment and track arbitrary objects. However, the recent Open-Vocabulary…

cs.CV2024★ 1 cited

Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

Bin Ren, Guofeng Mei, Danda Pani Paudel +6

Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cl…

cs.CV2024

Restore Anything Model via Efficient Degradation Adaptation

Bin Ren, Eduard Zamfir, Zongwei Wu +6

With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful. Traditional approaches ty…