{"ID":6536147,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10610","arxiv_id":"2607.10610","title":"WasteAssistant: Regulation-Guided Visual Question Answering Framework for Intelligent Waste Segregation and Sustainable Managemen","abstract":"Efficient waste segregation is critical for sustainable urban management and environmental governance. Existing automated systems are limited by single-modality visual processing, insufficient contextual understanding, and weak regulatory alignment. To address these issues, we propose a language-guided vision-AI framework that integrates vision-language models and multimodal large language models for joint visual-linguistic reasoning. This framework implements a visual question answering paradigm aligned with India's Solid Waste Management Rules 2016. We construct a new WasteVQA dataset with 13,500 question-answer pairs across 21 waste categories. Experiments show that the BLIP-based model achieves a BLEU score of 0.8291 and a BERTScore of 0.9273, outperforming traditional CNN-based methods. This work improves source-level segregation accuracy, ensures regulatory compliance, and supports scalable deployment for municipal and citizen-facing waste management, promoting multimodal AI in sustainable urban infrastructure. The source code and dataset are available at: https://github.com/Khushkataruka/WasteAssistant","short_abstract":"Efficient waste segregation is critical for sustainable urban management and environmental governance. Existing automated systems are limited by single-modality visual processing, insufficient contextual understanding, and weak regulatory alignment. To address these issues, we propose a language-guided vision-AI framew...","url_abs":"https://arxiv.org/abs/2607.10610","url_pdf":"https://arxiv.org/pdf/2607.10610v1","authors":"[\"Khush Kataruka\",\"Harshit Maurya\",\"Anuja Vats\",\"Murari Mandal\",\"Kiran Raja\",\"Praveen Kumar Chandaliya\"]","published":"2026-07-12T07:14:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\",\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":614140,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T01:21:01.169441415Z","DeletedAt":null,"paper_id":6536147,"paper_url":"https://arxiv.org/abs/2607.10610","paper_title":"WasteAssistant: Regulation-Guided Visual Question Answering Framework for Intelligent Waste Segregation and Sustainable Managemen","repo_url":"https://github.com/Khushkataruka/WasteAssistant","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}