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This makes properly localized bounding boxes degenerate during\niterative regression or even suppressed during NMS. In the paper we propose\nIoU-Net learning to predict the IoU between each detected bounding box and the\nmatched ground-truth. The network acquires this confidence of localization,\nwhich improves the NMS procedure by preserving accurately localized bounding\nboxes. Furthermore, an optimization-based bounding box refinement method is\nproposed, where the predicted IoU is formulated as the objective. Extensive\nexperiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well\nas its compatibility with and adaptivity to several state-of-the-art object\ndetectors.","url_abs":"http://arxiv.org/abs/1807.11590v1","url_pdf":"http://arxiv.org/pdf/1807.11590v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"acquisition-of-localization-confidence-for","repo_url":"https://github.com/vacancy/PreciseRoIPooling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"acquisition-of-localization-confidence-for","repo_url":"https://github.com/CSAILVision/unifiedparsing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"acquisition-of-localization-confidence-for","repo_url":"https://github.com/Qinhj07/ATOMCode","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"acquisition-of-localization-confidence-for","repo_url":"https://github.com/cameronthurston/CSAILVisionUpdated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"cascade-r-cnn","method_name":"Cascade R-CNN"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"iou-net","method_name":"IoU-Net"},{"method_slug":"iou-guided-nms","method_name":"IoU-guided NMS"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"precise-roi-pooling","method_name":"Precise RoI Pooling"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"sgd-with-momentum","method_name":"SGD with Momentum"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"step-decay","method_name":"Step Decay"}],"datasets_introduced":[],"methods_introduced":[{"slug":"iou-net","name":"IoU-Net","full_name":"IoU-Net"},{"slug":"iou-guided-nms","name":"IoU-guided NMS","full_name":"IoU-guided NMS"},{"slug":"precise-roi-pooling","name":"Precise RoI Pooling","full_name":"Precise RoI Pooling"}],"results":[{"leaderboard":"/sota/object-detection-on-coco","task":"Object Detection","dataset":"COCO test-dev","model":"IoU-Net","rank_in_archive_order":192,"of":225,"metrics":{"box mAP":"40.6"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.11590","atlas_url":"https://app.syntology.ai/?focus=1807.11590","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11590"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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