arXiv AI August 18, 2026 By Shanwen Wang, Xin Sun, Danfeng Hong, Junyu Dong, Patrick Le Callet

Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank

arXiv:2608. 16681v1 Announce Type: cross Abstract: Although semi-supervised semantic segmentation ($\text{S}^4$) utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data causes the former to dominate, which severely degrades pseudo-label quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

4d ago

Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation

arXiv:2609.37481v1 Announce Type: new Abstract: Large-scale pervasive sensing increasingly relies on high-resolution satellite imagery, yet task-specific onboard vision is constrained by costly annot...

By Ahmed Abdelnaby, Mohamed Elmahallawy, Marius Bernahrndt, Tobias Hecking

efficiency multimodal benchmarks

arXiv Computer Vision

Aug 28

DOD-SA: Infrared-Visible Decoupled Object Detection with Single-Modality Annotations

The paper introduces DOD-SA, a framework for infrared-visible object detection that uses only single-modality annotations. It employs a Collaborative Teacher-Student Network with a single-modality branch and a dual-modality decoupled branch to transfer knowledge across modalities, and a Progressive and Self‑Tuning Training Strategy to refine pseudo‑labels. A Pseudo Label Assigner is also designed to align labels between modalities during training.

By Hang Jin, Chenqiang Gao, Junjie Guo, Fangcen Liu, Qinyao Chang, Kanghui Tian, Deyu Meng

computer-vision

arXiv AI

Sep 15

Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning

arXiv:2609.14451v1 Announce Type: cross Abstract: Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the ps...

By Itai David, Daphna Weinshall

rag

arXiv Machine Learning

Jun 24

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

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

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 26

Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning

arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.

By Augustinas Ju\v{c}as, Yangchen Pan

computer-vision safety

arXiv AI

Sep 1

Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning

arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...

By Yue Cheng, Jiajun Zhang, Xiaohui Gao, Weiwei Xing, Zhanxing Zhu

efficiency benchmarks safety

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Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation

DOD-SA: Infrared-Visible Decoupled Object Detection with Single-Modality Annotations

Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning

Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning