https://github.com/adeboissiere/FUSION-human-action-recognition
공식 코드Infrared and 3D skeleton feature fusion for RGB-D action recognition
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 11
- 평가표
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- 보존된 평가 기록
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/adityac94/Grad_CAM_plus_plus
공식 코드Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/adrianiordache/mlfd
공식 코드Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets
https://github.com/aengusl/latent-adversarial-training
공식 코드Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
https://github.com/afperezm/acoustic-images-distillation
공식 코드https://github.com/againerju/tcg_recognition
공식 코드https://github.com/aheldis/Attack-on-Scene-Flow-using-Point-Clouds
공식 코드https://github.com/ahmed-nady/multimodal-action-recognition
공식 코드EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition
https://github.com/ahmetgunduz/Real-time-GesRec
공식 코드Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
https://github.com/ahoelzemann/annotationmatters
공식 코드https://github.com/ahoelzemann/hangtime_har
공식 코드https://github.com/ai-forever/ghost
공식 코드A new face swap method for image and video domains: a technical report
https://github.com/ai4life-group/splice
공식 코드Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
https://github.com/aikuniverse/tcte-net
공식 코드Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
https://github.com/aiotgroup/xrfv2
공식 코드https://github.com/airglow/gimme_signals_action_recognition
공식 코드Gimme Signals: Discriminative signal encoding for multimodal activity recognition
https://github.com/airsplay/vimpac
공식 코드VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
https://github.com/akosiorek/hart
공식 코드https://github.com/aleflabo/prego
공식 코드PREGO: online mistake detection in PRocedural EGOcentric videos
https://github.com/alessiogalatolo/diekae
공식 코드https://github.com/alexanderrichard/squirrel
공식 코드A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
https://github.com/alexandrosstergiou/Class_Feature_Visualization_Pyramid
공식 코드https://github.com/alexandrosstergiou/Inception_v3_TV_Human_Interactions
공식 코드https://github.com/alexandrosstergiou/SoftPool
공식 코드https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates
공식 코드Multi-Temporal Convolutions for Human Action Recognition in Videos
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates/blob/master/train_model.py
공식 코드Learn to cycle: Time-consistent feature discovery for action recognition
https://github.com/alexfrom0815/ir-bpp
공식 코드Learning Physically Realizable Skills for Online Packing of General 3D Shapes
https://github.com/alexrich021/3dvnet
공식 코드3DVNet: Multi-View Depth Prediction and Volumetric Refinement
https://github.com/alibaba-damo-academy/worldvla
공식 코드https://github.com/alibaba-mmai-research/HyRSM
공식 코드Hybrid Relation Guided Set Matching for Few-shot Action Recognition
https://github.com/alibaba-mmai-research/TAdaConv
공식 코드https://github.com/alibaba-mmai-research/clip-fsar
공식 코드CLIP-guided Prototype Modulating for Few-shot Action Recognition
https://github.com/alibaba-mmai-research/hyrsmplusplus
공식 코드HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
https://github.com/alibaba-mmai-research/masked-action-recognition
공식 코드https://github.com/alibaba-mmai-research/molo
공식 코드MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
https://github.com/alibaba-mmai-research/pytorch-video-understanding
공식 코드TAda! Temporally-Adaptive Convolutions for Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.