https://github.com/scu-zjz/imdlbenco
공식 코드IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
End-to-End Semantic Video Transformer for Zero-Shot Action Recognition
Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization
Multi class activity classification in videos using Motion History Image generation
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
Learning long-term dependencies for action recognition with a biologically-inspired deep network
LOGO: A Long-Form Video Dataset for Group Action Quality Assessment
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries Challenge
Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors
DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions
PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization
End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
Multimodal Task-Driven Dictionary Learning for Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.