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/ReshmikaD/Artistic-Style-Transfer-CNN
https://github.com/Roc-Ng/DeepMIL
https://github.com/RudreshVeerkhare/StyleGan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/S-aiueo32/lpips-pytorch
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/SSL92/hyperIQA
https://github.com/SUPERSHOPxyz/stylegan3-gradient
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/SafiyaJan/Attacking-Neural-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ShreyasArthur/AbnormalEventDetection
https://github.com/SilvioGiancola/SoccerNetv2-DevKit
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/SmartPorridge/google-AVA-Dataset-downloader
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/SpikeKing/VQA-v2
https://github.com/StanfordHCI/virtualhome
https://github.com/Sy-Zhang/MMC-PCFG
Learning a Grammar Inducer from Massive Uncurated Instructional Videos
https://github.com/TerenceLiu4444/Kaggle-solutions
Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge
https://github.com/The-AI-Summer/self-attention-cv
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/TonyYaoMSU/ProjectedGradientDescent
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/TooTouch/WhiteBox-Part1
https://github.com/TraffiX-VideoQA/TUMTraffic-VideoQA-Baseline
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
https://github.com/TranThienDat-Nguyen/VisionEvaluation
https://github.com/VQAssessment/FAST-VQA-and-FasterVQA
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
https://github.com/VainF/pytorch-msssim
https://github.com/VishaalMK/VectorDefense
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/WaqasSultani/AnomalyDetectionCVPR2018
https://github.com/WavesUR/embedded_TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/Whiffe/mmaction2_YF
https://github.com/XLearning-SCU/2024-ICLR-Norton
Multi-granularity Correspondence Learning from Long-term Noisy Videos
https://github.com/YHKimGithub/FATSnet
Temporally smooth online action detection using cycle-consistent future anticipation
https://github.com/YinDFY/PGD_for_targeted_attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Zoky-2020/Set-level_Guidance_Attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/abahram77/mnistChallenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/abahram77/mnist_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/acaelles97/devis
https://github.com/acewjh/Video-Swin-Transformer
https://github.com/adacheng/egothink
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
https://github.com/aim3-ruc/youmakeup_challenge2022
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.