JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.
By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
arXiv:2607. 18465v1 Announce Type: new Abstract: Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video.
By Ju Chen, Sijia Xu, Jun Feng, Zhiqiang Gao, Zhengyi Yang
The paper investigates the effect of using semantic positive pairs—different instances of the same class—in self‑supervised visual representation learning. By creating matched ImageNet‑1K subsets of augmented pairs and manually curated semantic pairs, the authors compare contrastive and non‑contrastive SSL methods under identical training conditions. Across transfer learning and object detection tasks, semantic‑pair pretraining consistently outperforms augmented‑pair pretraining, with contrastive methods like SimCLR showing the largest gains, indicating that semantic pairs foster additional invariances beyond standard augmentations.
By Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong
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.
arXiv:2512. 10244v2 Announce Type: replace-cross Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-specific examples to annotate the unlabeled ones.
By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong
The paper demonstrates that object detection benchmarks suffer from incomplete annotations, with re-annotation of COCO, Pascal VOC, Cityscapes, and KITTI revealing up to a 60% increase in detected objects, especially small, occluded, or densely packed instances. The authors propose a scalable annotation pipeline that uses multiple annotators per object to capture uncertainty and improve recall, and they introduce two new large-scale benchmarks: an uncertainty-aware detection benchmark and a label error detection benchmark based on real errors. Their findings show that benchmark performance is highly sensitive to annotation quality, yet model rankings remain largely unchanged, highlighting the need for uncertainty-aware evaluation to better reflect real-world ambiguity.
By Sarina Penquitt, Jonathan Klees, Antonia van Betteray, Parssa Jashnieh, Peter Stehr, Matthias Rottmann, Lars Schmarje
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.
By Jinshi Liu, Lei He, Pan Liu
arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.
By Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang
The paper introduces Dual-Stream Semantic Guidance (DSSG), a framework for Source‑Fully‑Free Domain Adaptation of Vision‑Language Models that mitigates dual semantic drift through a caption stream and a class‑anchor stream. It adds a Dynamic Cross‑Modal Knowledge Distillation module and a Prototype Anchor Calibration extension (DSSG‑PAC) to reduce computation while maintaining performance. Experiments show DSSG outperforms state‑of‑the‑art methods and DSSG‑PAC cuts adaptation time by 18.9% with minimal loss in accuracy.
By Weiwei Xiang, Shun Peng, Guangyi Xiao, Hao Chen, Lei Yang
arXiv:2608.29604v1 Announce Type: cross
Abstract: Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized sem...
By Siyi Liu, Xiaorong Zhu, Enjun Du, Xinyu Zuo, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, Yongqi Zhang
arXiv:2607. 23981v1 Announce Type: cross Abstract: Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes.
By Weijun Tian, Rui Liu
arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.
By Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham W. Taylor