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Action Recognition

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하위 태스크: 15
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.

In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.

Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.

논문

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Action Recognition

논문

Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features

Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing

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?

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

LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models

Safety Alignment via Constrained Knowledge Unlearning

Benchmarking and Rethinking Knowledge Editing for Large Language Models

Disentangling Knowledge Representations for Large Language Model Editing

Localizing Knowledge in Diffusion Transformers

Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action Recognition

Model Editing with Graph-Based External Memory

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

Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing

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

ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization

Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?

Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models

Reinforcement Learning meets Masked Video Modeling : Trajectory-Guided Adaptive Token Selection

DeltaEdit: Enhancing Sequential Editing in Large Language Models by Controlling Superimposed Noise

A Short Overview of Multi-Modal Wi-Fi Sensing

Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition

DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition

Vision and Intention Boost Large Language Model in Long-Term Action Anticipation

BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing

Direct Motion Models for Assessing Generated Videos