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20202026
most citedRobust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning

34 citations · 39 across the 8 of their papers we have counts for

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8 papers · 1 filter

cs.CV2026

Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents

Peng Huang, Yiming Wang, Yineng Chen +9

Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of echocardiographic data remains challe…

cs.CV2025

RLMiniStyler: Light-weight RL Style Agent for Arbitrary Sequential Neural Style Generation

Jing Hu, Chengming Feng, Shu Hu +4

Arbitrary style transfer aims to apply the style of any given artistic image to another content image. Still, existing deep learning-based methods often require significant computa…

cs.CV2025

Real-Time Animatable 2DGS-Avatars with Detail Enhancement from Monocular Videos

Xia Yuan, Hai Yuan, Wenyi Ge +3

High-quality, animatable 3D human avatar reconstruction from monocular videos offers significant potential for reducing reliance on complex hardware, making it highly practical for…

cs.CV2025

Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection

Shixuan Song, Hao Chen, Shu Hu +3

Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-t…

cs.CV2021

Transferable Adversarial Examples for Anchor Free Object Detection

Quanyu Liao, Xin Wang, Bin Kong +5

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a sur…

cs.CV2020

Fast Local Attack: Generating Local Adversarial Examples for Object Detectors

Quanyu Liao, Xin Wang, Bin Kong +4

The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses…