https://github.com/cvlab-kaist/SOLA
공식 코드Referring Video Object Segmentation via Language-aligned Track Selection
Papers with Code 아카이브
Scene Parsing
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Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cvmi-lab/pla
공식 코드https://github.com/cvmi-lab/slotmim
공식 코드A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
https://github.com/cxfyxl/viptr
공식 코드SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor
https://github.com/cxqnet/masseg
공식 코드https://github.com/cxx226/dpf
공식 코드https://github.com/cyuan-sjtu/vit-asym
공식 코드Surgical Scene Segmentation by Transformer With Asymmetric Feature Enhancement
https://github.com/d-rothen/bmhrl
공식 코드https://github.com/dali92002/SSL-OCR
공식 코드https://github.com/daniel3303/storyreasoning
공식 코드StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
https://github.com/daniellli/lion
공식 코드https://github.com/daniellli/pad
공식 코드https://github.com/danielshkao/thinkvideo
공식 코드ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of Thoughts
https://github.com/daoyuan98/sdt-hoi
공식 코드https://github.com/davidmchan/clair-a
공식 코드CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
https://github.com/dawn-lx/openvoc-vidvrd
공식 코드Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
https://github.com/dawn-lx/vidvrd-tracklets
공식 코드Video Relation Detection via Tracklet based Visual Transformer
https://github.com/declare-lab/emma-x
공식 코드https://github.com/deepinsight/insightface
공식 코드Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing
https://github.com/deeplab-ai/selfsupervisedvrd
공식 코드https://github.com/deepmind/dm_nevis
공식 코드NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
https://github.com/deepmind/threednel
공식 코드3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
https://github.com/deeptag/cstm
공식 코드Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation
https://github.com/deeptibhegde/clip-goes-3d
공식 코드CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
https://github.com/defaultrui/vln-ver
공식 코드Volumetric Environment Representation for Vision-Language Navigation
https://github.com/delair-ai/DISCA
공식 코드Interactive Learning for Semantic Segmentation in Earth Observation
https://github.com/denabazazian/SoftPHOC_TextDescriptor
공식 코드Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images
https://github.com/dengpingfan/csu
공식 코드https://github.com/dexianghong/manet
공식 코드Multi-Attention Network for Compressed Video Referring Object Segmentation
https://github.com/dibschat/tempAgg
공식 코드Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dipika-singhania/C2F-TCN
공식 코드Coarse to Fine Multi-Resolution Temporal Convolutional Network
https://github.com/djiajunustc/H-23D_R-CNN
공식 코드From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection
https://github.com/dk-liang/uniseg3d
공식 코드https://github.com/dlut-yyc/isomer
공식 코드Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation
https://github.com/docworlds/tsg-bench
공식 코드https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.