https://github.com/doc-doc/NExT-QA
공식 코드NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
Papers with Code 아카이브
Video
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 34
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/doc-doc/next-gqa
공식 코드Can I Trust Your Answer? Visually Grounded Video Question Answering
https://github.com/doc-doc/videoqa-llms
공식 코드https://github.com/domenicrosati/longform-evaluation-model-editing
공식 코드https://github.com/dominickrei/egoexo4adl
공식 코드https://github.com/dominickrei/pi-vit
공식 코드Just Add $π$! Pose Induced Video Transformers for Understanding Activities of Daily Living
https://github.com/dominickrei/poseawarevt
공식 코드Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers
https://github.com/dong03/MVSS-Net
공식 코드Image Manipulation Detection by Multi-View Multi-Scale Supervision
https://github.com/donglao/srt
공식 코드Sub-token ViT Embedding via Stochastic Resonance Transformers
https://github.com/dongqi-me/vista
공식 코드What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
https://github.com/dontfollowmeimcrazy/vot-kd-rl
공식 코드https://github.com/donydchen/mvsplat360
공식 코드MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
https://github.com/dot27/temporal-tessellation
공식 코드Temporal Tessellation: A Unified Approach for Video Analysis
https://github.com/dragonlzm/pave
공식 코드https://github.com/droliven/diverse_sampling
공식 코드Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space
https://github.com/ds-kiel/mcucoder
공식 코드MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices
https://github.com/dtamayo-nlp/memat
공식 코드Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
https://github.com/dualparal-project/dualparal
공식 코드https://github.com/dukebw/SSTVOS
공식 코드SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
https://github.com/dulucas/simlpe
공식 코드https://github.com/dun-research/3D-CSL
공식 코드3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval
https://github.com/dun-research/drca
공식 코드Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval
https://github.com/dun-research/haltingvt
공식 코드HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
https://github.com/dvl-tum/SUSHI
공식 코드Unifying Short and Long-Term Tracking with Graph Hierarchies
https://github.com/dvl-tum/e-osvos
공식 코드https://github.com/dvlab-research/LSDBench
공식 코드Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
https://github.com/dvlab-research/controlnext
공식 코드ControlNeXt: Powerful and Efficient Control for Image and Video Generation
https://github.com/dvlab-research/jenga
공식 코드Training-Free Efficient Video Generation via Dynamic Token Carving
https://github.com/dvlab-research/lisa
공식 코드https://github.com/dvlab-research/magicmirror
공식 코드Magic Mirror: ID-Preserved Video Generation in Video Diffusion Transformers
https://github.com/dvlab-research/proposereduce
공식 코드https://github.com/dvlab-research/vfiformer
공식 코드https://github.com/dvlab-research/visionzip
공식 코드VisionZip: Longer is Better but Not Necessary in Vision Language Models
https://github.com/dxli94/WLASL
공식 코드https://github.com/dyhBUPT/GIAOTracker
공식 코드https://github.com/dyhbupt/ensemblemot
공식 코드EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.