https://github.com/qiuzh20/tuning-keys-v.s.-values
공식 코드Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
Papers with Code 아카이브
Activity Recognition
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- 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/qiwang233/dailydvs-200
공식 코드DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition
https://github.com/qizhongtan/d2st-adapter
공식 코드D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
https://github.com/qizhou000/DAFNet
공식 코드DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
https://github.com/qizhou000/RECIPE
공식 코드Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
https://github.com/qizhou000/visedit
공식 코드https://github.com/quiil/quiil_thompson_solution
공식 코드https://github.com/quqxui/medlasa
공식 코드Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
https://github.com/radu1999/walkandtext
공식 코드Aligning Actions and Walking to LLM-Generated Textual Descriptions
https://github.com/rahmyyy/gavd
공식 코드Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset
https://github.com/rajammanabrolu/KG-A2C
공식 코드Graph Constrained Reinforcement Learning for Natural Language Action Spaces
https://github.com/rana2149/actnetformer
공식 코드ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
https://github.com/raphaelmemmesheimer/skeleton-dml
공식 코드Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition
https://github.com/raphaelmemmesheimer/sl-dml
공식 코드SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
https://github.com/rate-accuracy-mvcnn/main
공식 코드Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
https://github.com/rayabhisek123/autoregad-hgformer
공식 코드Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition
https://github.com/razzu/cmc-cmkm
공식 코드Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition
https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition
공식 코드https://github.com/realcrane/Defending-Black-box-Skeleton-based-Human-Activity-Classifiers
공식 코드Defending Black-box Skeleton-based Human Activity Classifiers
https://github.com/realcrane/SMART
공식 코드Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
https://github.com/realcrane/robustactionrecogniser
공식 코드Defending Black-box Skeleton-based Human Activity Classifiers
https://github.com/reallm-labs/infigui-r1
공식 코드InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
https://github.com/reddyav1/rocog-v2
공식 코드Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
https://github.com/reddyav1/unite
공식 코드Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training
https://github.com/redwang/DTGRM
공식 코드Temporal Relational Modeling with Self-Supervision for Action Segmentation
https://github.com/rhwxmx/eventmamba
공식 코드https://github.com/richardtml/divita
공식 코드https://github.com/ricky-xian/mitfas
공식 코드https://github.com/ricky-xian/pmi-sampler
공식 코드PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
https://github.com/rllab/rllab
공식 코드Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rmazzier/star
공식 코드Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
https://github.com/rmokady/JOKR
공식 코드JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
https://github.com/roberto1648/adversarial-perturbations-on-images-and-videos
공식 코드Targeted Nonlinear Adversarial Perturbations in Images and Videos
https://github.com/roeiherz/STAG-Nets
공식 코드https://github.com/rogetk/oddet
공식 코드https://github.com/rongchangli/zscar_c2c
공식 코드C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.