most citedMultimodal Conditional Information Bottleneck for Generalizable AI-Generated Image Detection

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cs.CV2026

DisDop: Distillation with Domain Priors for Open-Vocabulary Aerial Object Detection

Ruihao Xu, Yong Liu, Yansong Tang +6

With the widespread application of drones in recent years, object detection of aerial images has attracted increasing attention, especially open-vocabulary aerial detection which i…

cs.CV2026

Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking

Deyi Zhu, Yuji Wang, Yong Liu +4

Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects and challenging scenarios with…

cs.CV2026

UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

Yanran Zhang, Wenzhao Zheng, Yifei Li +5

In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fie…

cs.CV2025

NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization

Yifei Li, Haoyuan He, Yu Zheng +5

The accessibility surge and abuse risks of user-friendly image editing models have created an urgent need for generalizable, up-to-date methods for Image Manipulation Detection and…

cs.CV2025

QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection

Yanran Zhang, Bingyao Yu, Yu Zheng +5

The emergence of visual autoregressive (AR) models has revolutionized image generation while presenting new challenges for synthetic image detection. Unlike previous GAN or diffusi…

cs.CV2025

Learning Counterfactually Decoupled Attention for Open-World Model Attribution

Yu Zheng, Boyang Gong, Fanye Kong +6

In this paper, we propose a Counterfactually Decoupled Attention Learning (CDAL) method for open-world model attribution. Existing methods rely on handcrafted design of region part…