https://github.com/happyharrycn/actionformer_release
공식 코드ActionFormer: Localizing Moments of Actions with Transformers
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 0
- 보존된 평가 기록
- 0
A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/haroldchen19/vistadpo
공식 코드VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
https://github.com/hendrycks/emodiversity
공식 코드How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
https://github.com/henghuiding/MOSE-api
공식 코드PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
https://github.com/henghuiding/MeViS
공식 코드PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
https://github.com/henryhungle/mm_dst
공식 코드https://github.com/hibrahimozturk/temporal_anomaly_detection
공식 코드https://github.com/hkuds/videorag
공식 코드VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
https://github.com/hlchen23/verified
공식 코드VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
https://github.com/hmxiong/streamchat
공식 코드Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
https://github.com/hoar012/tdc-video
공식 코드Multimodal Long Video Modeling Based on Temporal Dynamic Context
https://github.com/hoi4d/PPTr
공식 코드Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding
https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/hongcheng-gao/haven
공식 코드https://github.com/hotfinda/videomambapro
공식 코드https://github.com/hs-yn/paver
공식 코드Panoramic Vision Transformer for Saliency Detection in 360° Videos
https://github.com/huangmozhi9527/conmh
공식 코드Contrastive Masked Autoencoders for Self-Supervised Video Hashing
https://github.com/hustvl/CrossVIS
공식 코드Crossover Learning for Fast Online Video Instance Segmentation
https://github.com/hutch-matt/vau-tutorial
공식 코드https://github.com/iai-hrc/ha-vid
공식 코드HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
https://github.com/ibm/sifar-pytorch
공식 코드https://github.com/iceclear/clip-iqa
공식 코드https://github.com/iigroup/ahiq
공식 코드Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network
https://github.com/iigroup/maniqa
공식 코드MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
https://github.com/inst-it/inst-it
공식 코드Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning
https://github.com/intellabs/multimodal_cognitive_ai
공식 코드ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/internlm/internlm-xcomposer
공식 코드https://github.com/intmegroup/love
공식 코드LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
https://github.com/iriya99/ovre
공식 코드https://github.com/isalirezag/tres
공식 코드No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
https://github.com/jacobswan1/Video2Commonsense
공식 코드Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
https://github.com/jakubmicorek/MULDE-Multiscale-Log-Density-Estimation-via-Denoising-Score-Matching-for-Video-Anomaly-Detection
공식 코드MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
https://github.com/jameszhou-gl/gpt-4v-distribution-shift
공식 코드How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
https://github.com/jasonrayshd/egomotion
공식 코드Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
https://github.com/jaykim9870/cvpr-22_loveu_unipyler
공식 코드https://github.com/jfightyr/slowfast-meet-vit
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.