{"slug": "webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch", "title": "Webly Supervised Multi-Label Recognition: Evaluation Benchmark and Dual-Branch Multi-Label Contrastive Learning", "summary": "Researchers have constructed a benchmark for webly supervised multi-label recognition (WS-MLR) including Web-COCO and Web-Pascal datasets, each containing about 300 thousand web images covering 80 and 20 categories respectively. They also propose a Dual-Branch Multi-Label Contrastive Learning (DBMLCL) framework that learns category-specific representations to identify and correct noisy labels, achieving superior performance over representative baselines.", "body_md": "arXiv:2607.20874v1 Announce Type: new\nAbstract: Training deep learning models with freely available web images can reduce their dependence on costly manual annotations. Although webly supervised learning has been widely studied for single-label recognition, its multi-label counterpart remains underexplored, partly due to the lack of unified benchmarks and fair comparison protocols. To address this gap, we construct a benchmark for webly supervised multi-label recognition (WS-MLR), including Web-COCO and Web-Pascal, and re-implement representative baselines under a unified setting. The two datasets cover the same 80 and 20 categories as MS-COCO and Pascal VOC, respectively, and contain about 300 thousand images retrieved from the Internet using category-word combinations as search keywords. We further propose a Dual-Branch Multi-Label Contrastive Learning (DBMLCL) framework, which learns category-specific instance-level and category-level representations together with their similarities to identify and correct noisy labels. Extensive experiments on the benchmark demonstrate that DBMLCL achieves superior performance compared to representative baselines.", "url": "https://wpnews.pro/news/webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch", "canonical_source": "https://arxiv.org/abs/2607.20874", "published_at": "2026-07-24 04:00:00+00:00", "updated_at": "2026-07-24 04:28:48.680861+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "large-language-models"], "entities": ["Web-COCO", "Web-Pascal", "MS-COCO", "Pascal VOC", "Dual-Branch Multi-Label Contrastive Learning (DBMLCL)"], "alternates": {"html": "https://wpnews.pro/news/webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch", "markdown": "https://wpnews.pro/news/webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch.md", "text": "https://wpnews.pro/news/webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch.txt", "jsonld": "https://wpnews.pro/news/webly-supervised-multi-label-recognition-evaluation-benchmark-and-dual-branch.jsonld"}}