The paper introduces Language-driven Dense Semantic Adaptor (LDSA) for multi-label image classification with incomplete annotations. LDSA leverages multimodal pretrained CLIP models to extract prior-adaptive relationships, employing a densely contrastive adaptor for visual contrastive constraints and a language-driven interactive decoder with class-specific prompt tuning. Experiments show LDSA achieves state‑of‑the‑art performance on public benchmarks and reveals implicit semantic relationships through its learning scheme.
By Cheng Chen, Yifan Zhao, Jia Li
arXiv:2608.22679v1 Announce Type: new
Abstract: Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as...
By Changki Sung, Hyungtae Lim, Wanhee Kim, Youngwoo Seo, Hyun Myung
arXiv:2603.02767v4 Announce Type: replace-cross
Abstract: Image--text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield repre...
By Hanpeng Liu, Zidan Wang, Shuoxi Zhang, Zonglin Zhao, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Yaqian Li, Kun He
arXiv:2512.15971v2 Announce Type: replace
Abstract: Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception u...
By Manuel Nkegoum, Minh-Tan Pham, \'Elisa Fromont, Bruno Avignon, S\'ebastien Lef\`evre
arXiv:2608. 15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust.
By Haadia Amjad, Ronald Tetzlaff
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.