https://github.com/164140757/scm
공식 코드Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Papers with Code 아카이브
Long-range modeling
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A new task for testing the long-sequence modeling capabilities and efficiency of language models.
Image credit: SCROLLS: Standardized CompaRison Over Long Language Sequences
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2760613195/km_unet
공식 코드https://github.com/AaronSpieler/elmneuron
공식 코드https://github.com/AutoLab-SAI-SJTU/MambaFusion
공식 코드MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
https://github.com/YubiaoYue/MedMamba
공식 코드https://github.com/ag1988/dss
공식 코드Diagonal State Spaces are as Effective as Structured State Spaces
https://github.com/bhattarailab/pathfinder-generator
공식 코드Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation
https://github.com/ceezh/llovi
공식 코드A Simple LLM Framework for Long-Range Video Question-Answering
https://github.com/ctlllll/sgconv
공식 코드What Makes Convolutional Models Great on Long Sequence Modeling?
https://github.com/dengyecode/t-former
공식 코드https://github.com/dengyecode/t-former_image_inpainting
공식 코드https://github.com/dslisleedh/ESC
공식 코드Emulating Self-attention with Convolution for Efficient Image Super-Resolution
https://github.com/facebookresearch/bart_ls
공식 코드Adapting Pretrained Text-to-Text Models for Long Text Sequences
https://github.com/facebookresearch/mega
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/long-range-arena
공식 코드https://github.com/google-research/longt5
공식 코드LongT5: Efficient Text-To-Text Transformer for Long Sequences
https://github.com/google-research/pegasus
공식 코드Investigating Efficiently Extending Transformers for Long Input Summarization
https://github.com/gravins/non-dissipative-propagation-CTDGs
공식 코드https://github.com/guo-stone/mambamorph
공식 코드MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
https://github.com/happinesslz/LION
공식 코드LION: Linear Group RNN for 3D Object Detection in Point Clouds
https://github.com/hazdzz/Converter
공식 코드https://github.com/hazyresearch/h3
공식 코드Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/hazyresearch/state-spaces
공식 코드Diagonal State Spaces are as Effective as Structured State Spaces
https://github.com/hbyecoding/u-rwkv
공식 코드U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
https://github.com/hh23333/tsd
공식 코드Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification
https://github.com/icandle/MAN
공식 코드Multi-scale Attention Network for Single Image Super-Resolution
https://github.com/jLooo/VADMamba
공식 코드VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
https://github.com/kenziyuliu/ms-g3d
공식 코드Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
https://github.com/kiaia/GIRAFFE
공식 코드GIRAFFE: Design Choices for Extending the Context Length of Visual Language Models
https://github.com/l1277471578/hybrid-emba3d
공식 코드https://github.com/leicheng-no/cdil-cnn
공식 코드Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
https://github.com/lewandofskee/mambaad
공식 코드MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
https://github.com/lhaof/nnmamba
공식 코드https://github.com/lindermanlab/S5
공식 코드https://github.com/lumia-group/fouriertransformer
공식 코드Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.