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Activity Recognition
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject Batch Editing for LLMs
Low-power Spike-based Wearable Analytics on RRAM Crossbars
Wandering around: A bioinspired approach to visual attention through object motion sensitivity
From Image to Video: An Empirical Study of Diffusion Representations
Reinforced Lifelong Editing for Language Models
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition
Injecting Universal Jailbreak Backdoors into LLMs in Minutes
AnyEdit: Edit Any Knowledge Encoded in Language Models
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject
Cross-Encoder Rediscovers a Semantic Variant of BM25
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity
MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream Fusion and Temporal Modeling
Large Language Models for Multi-Robot Systems: A Survey
HD-EPIC: A Highly-Detailed Egocentric Video Dataset
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition
Position: Editing Large Language Models Poses Serious Safety Risks
Scaling laws in wearable human activity recognition
Kronecker Mask and Interpretive Prompts are Language-Action Video Learners
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Flow Q-Learning
Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach
Lifelong Knowledge Editing requires Better Regularization
Building Bridges, Not Walls -- Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution
XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Action Recognition Using Temporal Shift Module and Ensemble Learning
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E
Optimizing Human Pose Estimation Through Focused Human and Joint Regions
Continuous signal sparse encoding using analog neuromorphic variability
MV-GMN: State Space Model for Multi-View Action Recognition
Can masking background and object reduce static bias for zero-shot action recognition?
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