Infrared small target detection (IRSTD) is a challenging task due to the extremely small size of targets, low signal-to-noise ratios, and heavy background clutter in infrared imagery. While the progress of deep learning has advanced this field, existing methods primarily adopt discriminative paradigms that directly map...
Xue-Lin Qian, Zi-Xuan Wang, Chang Xu et al.· 0 citations
The absence of accurate, noninvasive, scalable screening tools keeps early esophageal cancer (EC) detection a global health challenge. Although noncontrast computed tomography (NC CT) is widely accessible, the esophagus is a hollow tubular structure prone to collapse and motion artifacts, making small early malignant l...
Jian Zhou, Guangyu Guo, Jia-Wen Yao et al.· Nature Medicine· 0 citations
P2Fusion, a prior-guided distillation-based framework that reformulates IVIF via dual intrinsic prompts, distill image-intrinsic priors, thermal saliency and spatial quality, into learnable dynamic regulators, and enables the network to adaptively mediate modal competition through expert specialization.
Yi Shi, Huichao Xie, Yu-Qing Wang et al.· 0 citations
This work introduces Universal Referring, a generalized UAV referring task that jointly expands the query modality and the output cardinality, and presents UAV-URNet, a detection-style baseline that maps heterogeneous queries into a shared query space and predicts variable-size target sets through set prediction.
The sample efficiency and scalability of RL post-training for video MLLMs and introduces OraRL, a decoupled advantage estimator that scales with model size and data, surpassing its backbone from 0.8B to 9B and GRPO up to 100k prompts.
Yun-Heng Li, Guo-Hong Mu, Hao Li et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
