https://github.com/md-mohaiminul/objectstatechange
Is Space-Time Attention All You Need for Video Understanding?
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/mees/Adversarial-Skill-Networks
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
https://github.com/metalbubble/moments_models
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
https://github.com/microndla/mobilevitv3
https://github.com/microsoft/MLCVQA
Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs
https://github.com/microsoft/distance-learner
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/mihirp1/Deep_Learning_Project_CPSC8810
https://github.com/mindspore-ai/models/tree/master/official/cv/nima
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/minghangz/cpl
https://github.com/mingu6/action_seg_ot
Temporal Action Segmentation: An Analysis of Modern Techniques
https://github.com/mira-space/mira
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
https://github.com/mira-space/miradata
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
https://github.com/mlfoundations/open_flamingo
https://github.com/modadundun/ProDisc-VAD
https://github.com/mogadmd/YT8M
Learnable pooling with Context Gating for video classification
https://github.com/mryoo/pooled_time_series
https://github.com/mx-mark/videotransformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mx54039q/cnn-visualizing
https://github.com/naver-ai/dawin
Leveraging Temporal Contextualization for Video Action Recognition
https://github.com/ndb796/pytorch-adversarial-training-cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ndrwmlnk/awesome-benchmarks-for-physical-reasoning-ai
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
https://github.com/nguyentthong/temporal_recipe
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding
https://github.com/nguyetn89/Anomaly_detection_BMVC2019
https://github.com/nguyetn89/Anomaly_detection_ICCV2019
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
https://github.com/nikankind/Reproduce-Article-Representation-Flow-for-Action-Recognition-with-PaddlePaddle
https://github.com/niveditarahurkar/CS231N-ActionRecognition
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/nvlabs/eagle
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
https://github.com/olivesgatech/3DPSD-VQA
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
https://github.com/olivesgatech/UNIQUE-Project-Repository
https://github.com/olivesgatech/UNIQUE-Unsupervised-Image-Quality-Estimation
https://github.com/om-ai-lab/ZoomEye
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
https://github.com/oncescuandreea/audio-retrieval
QuerYD: A video dataset with high-quality text and audio narrations
https://github.com/open-mmlab/mmaction2
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/open-mmlab/mmdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.