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Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model
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Transformer-based segmentation methods face the challenge of efficient inference when dealing with high-resolution images. Recently, several linear attention architectures, such as Mamba and RWKV, have attracted much attention as they can process long sequences efficiently. In this work, we focus on designing an efficient segment-anything model by exploring these different architectures. Specifically, we design a mixed backbone that contains convolution and RWKV operation, which achieves the best for both accuracy and efficiency. In addition, we design an efficient decoder to utilize the multiscale tokens to obtain high-quality masks. We denote our method as RWKV-SAM, a simple, effective, fast baseline for SAM-like models. Moreover, we build a benchmark containing various high-quality segmentation datasets and jointly train one efficient yet high-quality segmentation model using this benchmark. Based on the benchmark results, our RWKV-SAM achieves outstanding performance in efficiency and segmentation quality compared to transformers and other linear attention models. For example, compared with the same-scale transformer model, RWKV-SAM achieves more than 2x speedup and can achieve better segmentation performance on various datasets. In addition, RWKV-SAM outperforms recent vision Mamba models with better classification and semantic segmentation results. Code and models will be publicly available.
Forward citations
Cited by 8 Pith papers
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PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
PointDGRWKV applies RWKV-like attention to domain-generalized point cloud classification, adding a geometric token shift and key-distribution alignment, and reports state-of-the-art accuracy on PointDA-10 and PointDG-3to1.
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GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution
A dual-branch RWKV-CNN super-resolution network with a multi-scale wavelet loss reports up to 0.11 dB PSNR gain over HAT on remote sensing benchmarks at a fraction of the compute.
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StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture
StyleRWKV applies recurrent RWKV-style attention with deformable shifting and skip scanning to achieve fast, high-quality arbitrary style transfer.
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EMOv2: Pushing 5M Vision Model Frontier
A 5M-parameter backbone with shared-weight spanning window attention sets new accuracy records across classification, detection, and generation benchmarks.
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Med-URWKV{\dag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation
Pretrained pure VRWKV encoders paired with pure VRWKV decoders match or beat CNN, ViT, and Mamba baselines, with a small model plus FAWA and MSCF modules reaching 88% average Dice.
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Linear Attention Modeling for Learned Image Compression
LALIC replaces transformer and Mamba blocks in learned image compression with bidirectional RWKV linear-attention blocks, reporting BD-rate gains over VTM-9.1 while keeping decoder latency moderate.
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Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction
OVS-Net, a SAM-based vessel segmentation framework with a micro-vessel enhancement branch and morphology-correction post-processing, reports higher Dice and better connectivity than six SAM baselines and 17 expert mod...
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A Survey of RWKV
A review of the RWKV architecture, its versions, applications, benchmarks, and open-source ecosystem; it presents no new experimental results.
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