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Mamba
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hp-l33/aim
공식 코드https://github.com/hrdhrd/MCO-E-Net
공식 코드https://github.com/hrtang22/MUSE
공식 코드MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
https://github.com/huanggh531/ejim
공식 코드Efficient Explicit Joint-level Interaction Modeling with Mamba for Text-guided HOI Generation
https://github.com/huangzizheng01/shufflemamba
공식 코드Stochastic Layer-Wise Shuffle: A Good Practice to Improve Vision Mamba Training
https://github.com/humansensinglab/Hamba
공식 코드Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
https://github.com/hunto/localmamba
공식 코드LocalMamba: Visual State Space Model with Windowed Selective Scan
https://github.com/hustvl/MaTVLM
공식 코드MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language Modeling
https://github.com/hustvl/dig
공식 코드DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
https://github.com/hustvl/mmmamba
공식 코드Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation
https://github.com/hustvl/omnimamba
공식 코드OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models
https://github.com/hustvl/vim
공식 코드Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/hzai-zjnu/mamba-yolo
공식 코드Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
https://github.com/hzlbbfrog/crackmamba
공식 코드https://github.com/icon-lab/I2I-Mamba
공식 코드I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
https://github.com/idiap/linearize-distill-pretrained-transformers
공식 코드Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity
https://github.com/ilyesbenaissa/MAAT
공식 코드MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy for time series
https://github.com/in-context-learning-2024/in-context
공식 코드https://github.com/intellabs/hardware-aware-automated-machine-learning
공식 코드Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models
https://github.com/irmvlab/mmtwin
공식 코드Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction
https://github.com/irmvlab/point-mamba
공식 코드https://github.com/isbrycee/t-mamba
공식 코드T-Mamba: Frequency-Enhanced Gated Long-Range Dependency for Tooth 3D CBCT Segmentation
https://github.com/isyangshu/mambamil
공식 코드MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology
https://github.com/itsdaniele/speculative_mamba
공식 코드The Mamba in the Llama: Distilling and Accelerating Hybrid Models
https://github.com/jLooo/VADMamba
공식 코드VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
https://github.com/jacklishufan/mamba-nd
공식 코드Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
https://github.com/jayparanjape/m-cd
공식 코드A Mamba-based Siamese Network for Remote Sensing Change Detection
https://github.com/jbr-ai-labs/mamba
공식 코드https://github.com/jcruan519/vm-unet
공식 코드https://github.com/jeming-creater/hwa-unetr
공식 코드HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
https://github.com/jiahuadong/dhm
공식 코드Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging
https://github.com/jialewei/onebev
공식 코드OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
https://github.com/jiarunliu/swin-umamba
공식 코드Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
https://github.com/jiehu1996/deformablemamba
공식 코드https://github.com/jinchengzhanggg/proffusion
공식 코드Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.