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cs.CV2026

Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction

Chaoqun He, Mingyang Xiang, Yingjing Xu +5

Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming inputs and respond at approp…

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

CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection

Zexi Jia, Zhiqiang Yuan, Xiaoyue Duan +3

AI-generated image detection faces a persistent trade-off between generalization and efficiency: lightweight artifact-based methods often degrade on unseen generators or domains, w…

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

Cross-Domain Vessel Segmentation via Latent Similarity Mining and Iterative Co-Optimization

Zhanqiang Guo, Jianjiang Feng, Jie Zhou

Retinal vessel segmentation serves as a critical prerequisite for automated diagnosis of retinal pathologies. While recent advances in Convolutional Neural Networks (CNNs) have dem…

cs.CV2026

Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations

Boyang Gong, Yu Zheng, Fanye Kong +2

While multimodal large language models demonstrate strong entity-level perception, faithfully grounding relational interactions between objects remains a persistent challenge. Alth…

cs.CV2024

UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation

Chaoqun Du, Yulin Wang, Jiayi Guo +3

Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers ha…