{"url":"/method/cabinet","slug":"cabinet","name":"CABiNet","full_name":"Context Aggregated Bi-lateral Network for Semantic Segmentation","full_name_withheld":false,"description_markdown":"With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5% with high execution speed (15 FPS).","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":null,"title":null,"url_on_a_paper_host":false},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Semantic Segmentation Models","url":"/methods/category/semantic-segmentation-models","pwc_aliases":["segmentation-models"]}],"n_papers_tagged":23,"archive_num_papers":23,"papers_newest_first":[{"paper":null,"title":"A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects","date":"2025-02-19","arxiv_id":"2502.13964","n_code_links":0,"syntology":null},{"paper":null,"title":"From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach","date":"2024-12-16","arxiv_id":"2412.11892","n_code_links":0,"syntology":null},{"paper":null,"title":"Refined and Segmented Price Sentiment Indices from Survey Comments","date":"2024-11-15","arxiv_id":"2411.09937","n_code_links":0,"syntology":null},{"paper":"/paper/robot-utility-models-general-policies-for","title":"Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments","date":"2024-09-09","arxiv_id":"2409.05865","n_code_links":1,"syntology":null},{"paper":"/paper/cabinet-content-relevance-based-noise","title":"CABINET: Content Relevance based Noise Reduction for Table Question Answering","date":"2024-02-02","arxiv_id":"2402.01155","n_code_links":1,"syntology":{"ran":3,"of":5,"unverified":2,"pointer_only":5}},{"paper":null,"title":"AccessLens: Auto-detecting Inaccessibility of Everyday Objects","date":"2024-01-29","arxiv_id":"2401.15996","n_code_links":0,"syntology":null},{"paper":null,"title":"BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images","date":"2024-01-18","arxiv_id":"2401.09791","n_code_links":0,"syntology":null},{"paper":null,"title":"Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation","date":"2024-01-04","arxiv_id":"2401.02117","n_code_links":0,"syntology":null},{"paper":null,"title":"USA-Net: Unified Semantic and Affordance Representations for Robot Memory","date":"2023-04-24","arxiv_id":"2304.12164","n_code_links":0,"syntology":null},{"paper":null,"title":"CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation","date":"2023-04-18","arxiv_id":"2304.09302","n_code_links":0,"syntology":null},{"paper":"/paper/orbit-a-unified-simulation-framework-for","title":"Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments","date":"2023-01-10","arxiv_id":"2301.04195","n_code_links":1,"syntology":{"ran":0,"of":12,"unverified":12,"pointer_only":0}},{"paper":null,"title":"OpenD: A Benchmark for Language-Driven Door and Drawer Opening","date":"2022-12-10","arxiv_id":"2212.05211","n_code_links":0,"syntology":null},{"paper":null,"title":"Toward an Intelligent Tutoring System for Argument Mining in Legal Texts","date":"2022-10-24","arxiv_id":"2210.13635","n_code_links":0,"syntology":null},{"paper":null,"title":"Robot Active Neural Sensing and Planning in Unknown Cluttered Environments","date":"2022-08-23","arxiv_id":"2208.11079","n_code_links":0,"syntology":null},{"paper":null,"title":"MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI","date":"2022-04-14","arxiv_id":"2204.06681","n_code_links":0,"syntology":null},{"paper":null,"title":"Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets","date":"2022-02-21","arxiv_id":"2202.10348","n_code_links":0,"syntology":null},{"paper":null,"title":"Modeling Long-horizon Tasks as Sequential Interaction Landscapes","date":"2020-06-08","arxiv_id":"2006.04843","n_code_links":0,"syntology":null},{"paper":null,"title":"Who mentions whom? 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