Hugging Face Trending Papers

Progressive Pseudo-Label Optimization for Point-Supervised Change Detection

The paper introduces a two‑stage framework for point‑supervised change detection that leverages SAM2 priors to generate object‑aware candidate masks from sparse point annotations. In Stage I, a mask selection strategy converts generic segmentation outputs into reliable change pseudo‑labels, followed by a lightweight CNN refinement module with an uncertainty‑aware loss to enhance boundary quality. Stage II employs a teacher‑student self‑training loop, where the teacher is updated via exponential moving average and periodically refreshes pseudo‑labels, creating a closed‑loop optimization that alternates between pseudo‑label refinement and model re‑optimization. Experiments on WHU‑CD, LEVIR‑CD, and SYSU‑CD show the method surpasses prior weakly supervised approaches and competes with several fully supervised methods.

arXiv Computer Vision

Sep 3

Progressive Pseudo-Label Optimization for Point-Supervised Change Detection

The paper introduces a two-stage framework for point-supervised change detection that leverages SAM2 priors to generate object-aware candidate masks and refines them with a lightweight CNN and uncertainty-aware loss. In the second stage, a teacher‑student self‑training loop with exponential moving average updates continuously improves pseudo‑labels and model performance. Experiments on WHU-CD, LEVIR-CD, and SYSU-CD show the method surpasses prior weakly supervised approaches and competes with fully supervised ones.

By Hailong Ning, Hao Wang, Yimeng Wang, Tao Lei, Renwei Dian, Asoke K. Nandi

computer-vision benchmarks

arXiv AI

Jun 30

CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation

arXiv:2606. 28724v1 Announce Type: cross Abstract: Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing.

By Jinhong Hu, Xiaoping Wang, Shuyin Huang, Guojin Zhong, Kaitai Liu, Kai Lu

llms computer-vision multimodal benchmarks

arXiv AI

Jul 24

U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation

arXiv:2607. 20705v1 Announce Type: cross Abstract: Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly.

By Elijah Danquah Darko, Min Xian, Terence Soule, Tiankai Yao, Matthew William Anderson

computer-vision benchmarks safety

arXiv AI

Jul 10

TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation

arXiv:2607. 08201v1 Announce Type: cross Abstract: Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity.

By Hyeonseop Song, Seokhun Choi, Hoseok Do

diffusion computer-vision benchmarks

arXiv AI

Jul 7

FM-ChangeNet: Learning Change through Pathwise Feature Transport

arXiv:2607. 04750v1 Announce Type: new Abstract: We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison.

By Roie Kazoom, George Leifman, Genady Beryozkin

computer-vision benchmarks safety

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 10

Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes

arXiv:2609.10356v1 Announce Type: new Abstract: Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban i...

By Benedetta Liberatori, Nermin Samet, Paolo Rota, Matthieu Cord, Elisa Ricci, Andrei Bursuc, Monika Wysocza\'nska

benchmarks

arXiv AI

Jul 1

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models

arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.

By Nikolai R\"ohrich, Julian Glei{\ss}ner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber

agents diffusion computer-vision fine-tuning efficiency safety

arXiv Computer Vision

3d ago

ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation

arXiv:2609.14943v1 Announce Type: new Abstract: Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholdi...

By Hongyu Liu, Yinlong Wang, Lusha Li, Hui Meng

computer-vision

arXiv Computer Vision

Aug 24

Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions

The paper surveys Continual Test-Time Adaptation (CTTA), a framework that adapts pretrained computer‑vision models to non‑stationary target distributions without source data or labeled targets, while avoiding catastrophic forgetting and error accumulation. It formally defines the CTTA problem, categorizes existing methods into optimization‑based, parameter‑efficient, and architecture‑based families, and reviews representative techniques and benchmarks across standard evaluation settings. The survey also outlines current limitations and proposes future research directions, such as adapting foundation models and black‑box systems.

By Sarthak Kumar Maharana, Shambhavi Mishra, Yunbei Zhang, Shuaicheng Niu, Taki Hasan Rafi, Jihun Hamm, Marco Pedersoli, Jose Dolz, Yunhui Guo

llms computer-vision benchmarks

arXiv AI

Jul 21

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background

arXiv:2607. 16705v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations.

By Shao-feng Jiang, Zhe-yang Jing, Qin Lu, Huan-huan Shi, Zhen Chen, Cong-xuan zhang, Chen Yi

computer-vision benchmarks safety

arXiv Computer Vision

Sep 2

Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

The paper presents a semi‑supervised biomedical image segmentation method that uses a diffusion‑based teacher–student framework. The teacher is pretrained via unsupervised diffusion reconstruction and then co‑trained with a student, leveraging supervised labels and cross pseudo‑supervision on unlabeled data. A multi‑round extension generates multiple stochastic reconstructions to further refine pseudo‑labels, achieving competitive or superior results on several 2D and 3D biomedical datasets, especially when labels are scarce.

By Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi

diffusion computer-vision benchmarks safety

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