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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.
Label Leakage in Federated Inertial-based Human Activity Recognition
CaseEdit: Enhancing Localized Commonsense Reasoning via Null-Space Constrained Knowledge Editing in Small Parameter Language Models
The Role of Video Generation in Enhancing Data-Limited Action Understanding
DocMEdit: Towards Document-Level Model Editing
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge
PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises
LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models
SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks
Safety Alignment via Constrained Knowledge Unlearning
ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos
Benchmarking and Rethinking Knowledge Editing for Large Language Models
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection
Disentangling Knowledge Representations for Large Language Model Editing
Localizing Knowledge in Diffusion Transformers
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action Recognition
Model Editing with Graph-Based External Memory
SPAR: Self-supervised Placement-Aware Representation Learning for Multi-Node IoT Systems
Leveraging Foundation Models for Multimodal Graph-Based Action Recognition
UniErase: Unlearning Token as a Universal Erasure Primitive for Language Models
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing
Egocentric Action-aware Inertial Localization in Point Clouds
Editing Across Languages: A Survey of Multilingual Knowledge Editing
UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing
FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments
UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models
NAMET: Robust Massive Model Editing via Noise-Aware Memory Optimization
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal Alignment
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