{"id":253,"date":"2024-12-12T23:58:05","date_gmt":"2024-12-12T15:58:05","guid":{"rendered":"https:\/\/511cvlab.sinkers.cn\/?p=253"},"modified":"2026-01-06T16:13:26","modified_gmt":"2026-01-06T08:13:26","slug":"sdpnet","status":"publish","type":"post","link":"https:\/\/cv.nirc.top\/zh\/2024\/sdpnet\/","title":{"rendered":"Dynamic Support Information Mining for Category-Agnostic Pose Estimation"},"content":{"rendered":"<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-5d9360ba wp-block-group-is-layout-constrained\" style=\"padding-right:5%;padding-left:5%\">\n<p class=\"has-text-align-center\">State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications<\/p>\n\n\n\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-block-group-is-layout-constrained\">\n    <div\n        class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-1 wp-block-buttons-is-layout-flex\">\n        <div class=\"wp-block-button\" style=\"line-height: 1.5;\">\n            <a class=\"wp-block-button__link wp-element-button\"  target=\"_blank\"\n                href=\"https:\/\/openaccess.thecvf.com\/content\/CVPR2024\/papers\/Ren_Dynamic_Support_Information_Mining_for_Category-Agnostic_Pose_Estimation_CVPR_2024_paper.pdf\"\n                style=\"padding-right: var(--wp--preset--spacing--40); padding-left: var(--wp--preset--spacing--40); display: flex; align-items: center; gap: 8px;\">\n                <div>\n                    <svg class=\"svg-inline--fa fa-file-pdf fa-w-12\" aria-hidden=\"true\" focusable=\"false\"\n                        data-prefix=\"fas\" data-icon=\"file-pdf\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"\n                        viewbox=\"0 0 384 512\" style=\"height: 1em; width: 1em;\">\n                        <path fill=\"#FFFFFF\"\n                            d=\"M181.9 256.1c-5-16-4.9-46.9-2-46.9 8.4 0 7.6 36.9 2 46.9zm-1.7 47.2c-7.7 20.2-17.3 43.3-28.4 62.7 18.3-7 39-17.2 62.9-21.9-12.7-9.6-24.9-23.4-34.5-40.8zM86.1 428.1c0 .8 13.2-5.4 34.9-40.2-6.7 6.3-29.1 24.5-34.9 40.2zM248 160h136v328c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V24C0 10.7 10.7 0 24 0h200v136c0 13.2 10.8 24 24 24zm-8 171.8c-20-12.2-33.3-29-42.7-53.8 4.5-18.5 11.6-46.6 6.2-64.2-4.7-29.4-42.4-26.5-47.8-6.8-5 18.3-.4 44.1 8.1 77-11.6 27.6-28.7 64.6-40.8 85.8-.1 0-.1.1-.2.1-27.1 13.9-73.6 44.5-54.5 68 5.6 6.9 16 10 21.5 10 17.9 0 35.7-18 61.1-61.8 25.8-8.5 54.1-19.1 79-23.2 21.7 11.8 47.1 19.5 64 19.5 29.2 0 31.2-32 19.7-43.4-13.9-13.6-54.3-9.7-73.6-7.2zM377 105L279 7c-4.5-4.5-10.6-7-17-7h-6v128h128v-6.1c0-6.3-2.5-12.4-7-16.9zm-74.1 255.3c4.1-2.7-2.5-11.9-42.8-9 37.1 15.8 42.8 9 42.8 9z\">\n                        <\/path>\n                    <\/svg>\n                <\/div>\n                <div>Paper<\/div>\n            <\/a>\n        <\/div>\n\n\n\n\n    <\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p class=\"text-justify has-small-font-size\" style=\"font-style:normal;font-weight:300;line-height:1.3\">Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations. Existing methods achieve the localization of arbitrary keypoints through similarity matching between support keypoint features and query image features. However, these methods primarily focus on mining information from the query images, neglecting the fact that support samples with keypoint annotations contain rich category-specific fine-grained semantic information and prior structural information. In this paper, we propose a Support-based Dynamic Perception Network (SDPNet) for the robust and accurate CAPE. On the one hand, SDPNet models complex dependencies between support keypoints, constructing category-specific prior structure to guide the interaction of query keypoints. On the other hand, SDPNet extracts fine-grained semantic information from support samples, dynamically modulating the refinement process of query.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Overview<\/h2>\n\n\n\n<div class=\"wp-block-columns are-vertically-aligned-center is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:44%\">\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8d199b01fd&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8d199b01fd\" class=\"wp-block-image size-large wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/sinkers-pic.oss-cn-beijing.aliyuncs.com\/img\/sdp-WechatIMG138.jpg\" alt=\"\"\/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"\u653e\u5927\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewbox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:55%\">\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8d199b0660&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8d199b0660\" class=\"wp-block-image size-large wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/sinkers-pic.oss-cn-beijing.aliyuncs.com\/img\/sdp-WechatIMG139.jpg\" alt=\"\"\/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"\u653e\u5927\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewbox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-text-align-center has-x-small-font-size\">Comparison with existing methods. Previous methods mainly focus on information mining of query samples. Our method fully explores the support information, allowing support features to deeply participate in the iterative refinement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Qualitative results<\/h2>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69f8d199b0bb8&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69f8d199b0bb8\" class=\"wp-block-image size-large wp-lightbox-container\"><img decoding=\"async\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/sinkers-pic.oss-cn-beijing.aliyuncs.com\/img\/Ren_Dynamic_Support_Information_Mining_for_Category-Agnostic_Pose_Estimation_CVPR_2024_paper_07.png\" alt=\"\"\/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"\u653e\u5927\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewbox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">We visualize the keypoint predictions under 5-shot setting. For simplicity, we only show one support image.<br>The bones are not the results predicted by our network, but provided by the dataset.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Bibtex<\/h2>\n\n\n\n<pre class=\"wp-block-code has-x-small-font-size\"><code>@inproceedings{ren2024dynamic,\n  title={Dynamic Support Information Mining for Category-Agnostic Pose Estimation},\n  author={Ren, Pengfei and Gao, Yuanyuan and Sun, Haifeng and Qi, Qi and Wang, Jingyu and Liao, Jianxin},\n  booktitle={Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition},\n  pages={1921--1930},\n  year={2024}\n}<\/code><\/pre>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Paper Abstract Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations. Existing methods achieve the localization of arbitrary keypoints through similarity matching between support keypoint features and query image [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":266,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[20],"tags":[],"class_list":["post-253","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cape"],"acf":{"writer":{"simple_value_formatted":"<code><em>This data type is not supported! 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