https://github.com/facebookresearch/EgoVLPv2
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/facebookresearch/videoalignment
https://github.com/farhadsh1992/stlpips_metric_tflite
https://github.com/fastiqa/fastiqa
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
https://github.com/fiabdu/Commonly-Interesting-Images
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
https://github.com/fluque1995/tfm-anomaly-detection
https://github.com/francois-rozet/piqa
https://github.com/gfxdisp/FovVideoVDP
https://github.com/google-research-datasets/eev
EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
https://github.com/google-research/google-research/tree/master/assemblenet
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
https://github.com/google-research/google-research/tree/master/poem
https://github.com/google-research/google-research/tree/master/tcc
https://github.com/gt-rail/rail_tasksim
https://github.com/gukyeongkwon/3DPSD-VQA
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
https://github.com/gulucaptain/dynamictrl
CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
https://github.com/h4nwei/SPAQ
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/haochenheheda/lvvis
https://github.com/haofanwang/video-swin-transformer-pytorch
https://github.com/happen2me/cross-gnn
https://github.com/henry8527/GCE
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hitsz-tmg/videovista
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
https://github.com/hongyuanyu/span
VideoMamba: State Space Model for Efficient Video Understanding
https://github.com/hope-yao/robust_attention_cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/huangwl18/language-planner
https://github.com/huggingface/transformers
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/idealo/image-quality-assessment
https://github.com/idearibosome/tf-perceptual-eusr
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/imrahulr/adversarial_robustness_pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/innat/UniFormerV2
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer
https://github.com/innat/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/innat/VideoSwin
https://github.com/isaacschaal/SG_training
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/jarikorhonen/nr-vqa-consumervideo
https://github.com/jeffreyhuang1/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.