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/bytedance/LatentSync
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/cassava-math-ubb/experiments
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/cdluminate/advrank
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cdluminate/advrank-pub
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/cg1177/dcan
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
https://github.com/chendelong1999/subobjects
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
https://github.com/chitwansaharia/HACAModel
https://github.com/chunfeng0301/Flow
https://github.com/cleverhans-lab/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/code-implementation1/Code1/tree/main/DDM
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
https://github.com/code-implementation1/Code1/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code2/tree/main/AdderNGD
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code6/tree/main/nasnet
https://github.com/code-implementation1/Code6/tree/main/nima
https://github.com/code-implementation1/Code6/tree/main/nima_vgg16
https://github.com/codegoat24/unifiedreward
Unified Reward Model for Multimodal Understanding and Generation
https://github.com/cs-giung/course-dl-TP
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/da-head0/cctv-anomaly-detection
https://github.com/dacostaHugo/Adversarial_attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/damo-nlp-sg/videollama2
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/datalass1/fastai
https://github.com/davide-coccomini/TimeSformer-Video-Classification
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/dineshssdn-867/Anomaly-Detection-CVPR2018
https://github.com/dipika-singhania/multi-scale-action-banks
Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/doc-doc/NExT-OE
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
https://github.com/dsl-lab/fvmd-frechet-video-motion-distance
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ejlb/squeezenet-chainer
https://github.com/ekosman/AnomalyDetectionCVPR2018-Pytorch
https://github.com/eladb3/orvit
https://github.com/eldadp100/Towards-Deep-Learning-Models-Resistant-to-Adversarial-Attacks-Implementation
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/eldadp100/cnn_course_final
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/eynaij/X-Temporal_catdim
https://github.com/facebookresearch/DetectAndTrack
https://github.com/facebookresearch/EgoTV
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.