{"id":83,"date":"2023-12-09T04:59:39","date_gmt":"2023-12-09T04:59:39","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/?page_id=83"},"modified":"2023-12-09T05:54:22","modified_gmt":"2023-12-09T05:54:22","slug":"spring-2023","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/spring-2023\/","title":{"rendered":"Spring 2023"},"content":{"rendered":"\n<p>In this page, we briefly introduce our work in Spring 2023. <\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Title<\/strong><\/p>\n\n\n\n<p>TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Links<\/strong><\/p>\n\n\n\n<p>[<a href=\"https:\/\/arxiv.org\/pdf\/2311.00660.pdf\">Paper<\/a>] [<a href=\"https:\/\/github.com\/ShenZheng2000\/TPSeNCE\/\">Code<\/a>] [<a href=\"https:\/\/shenzheng2000.github.io\/images\/TPSeNCE\/TPSeNCE_Slides.pdf\">Slides<\/a>] [<a href=\"https:\/\/www.youtube.com\/watch?v=eNS_8fuSLjc\">Videos<\/a>]<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Abstract<\/strong><\/p>\n\n\n\n<p>Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce artifacts and distortions and struggle to control the amount of rain generated due to a lack of proper constraints. In this paper, we propose an unpaired image-to-image translation framework for generating realistic rainy images. We first introduce a Triangular Probability Similarity (TPS) constraint to guide the generated images toward clear and rainy images in the discriminator manifold, thereby minimizing artifacts and distortions during rain generation. Unlike conventional contrastive learning approaches, which indiscriminately push negative samples away from the anchors, we propose a Semantic Noise Contrastive Estimation (SeNCE) strategy and reassess the pushing force of negative samples based on the semantic similarity between the clear and the rainy images and the feature similarity between the anchor and the negative samples. Experiments demonstrate realistic rain generation with minimal artifacts and distortions, which benefits image deraining and object detection in rain. Furthermore, the method can be used to generate realistic snowy and night images, underscoring its potential for broader applicability.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Contributions<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Triangular Probability Similarity (TPS) for minimizing artifacts and distortions.<\/li>\n\n\n\n<li>Semantic Noise Contrastive Estimation (SeNCE) for optimizing the amounts of generated rain.<\/li>\n\n\n\n<li>Realistic rain generation benefits deraining and object detection in real rainy conditions.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Future Works<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Extremely Heavy Rain<\/li>\n\n\n\n<li>Strong Light Sources<\/li>\n\n\n\n<li>Diffusion Models<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Brief Workflow<\/strong><\/p>\n\n\n\n<p>TPS aligns the generated rainy image with the clear and rainy image in the discriminator<br>manifold to suppress artifacts and distortions, while SeNCE adjusts the pushing force of the negative patches based on their feature similarities with the anchor patch and refines that force with the semantic similarity between clear and rainy<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"873\" height=\"778\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image.png\" alt=\"\" class=\"wp-image-85\" style=\"width:674px;height:auto\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image.png 873w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-300x267.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-768x684.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Detailed Workflow<\/strong><\/p>\n\n\n\n<p>TPSeNCE uses a generator to translate clear images to rainy ones, a discriminator with TPS and GAN losses, and an encoder that embeds patches from both clear and generated images. MLPs process these patches contrastively to output SeNCE loss, guided by semantic segmentation maps.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"581\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-1-1024x581.png\" alt=\"\" class=\"wp-image-86\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-1-1024x581.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-1-300x170.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-1-768x436.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-1.png 1356w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>TPS: Motivation<\/strong><\/p>\n\n\n\n<p>T-SNE Visualization of the output matrices from the discriminator.  We notice that &#8216;Good&#8217; generated images has more feature overlap with clear and (real) rainy images. Designing a loss that maximize feature overlap between D(Z) and D(X) and D(Y) would minimize the space for artifacts and distortions, and thereby improve the quality of the generated image. <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"778\" height=\"741\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-2.png\" alt=\"\" class=\"wp-image-87\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-2.png 778w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-2-300x286.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-2-768x731.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>TPS: Formulas<\/strong><\/p>\n\n\n\n<p>Based on the triangle inequality theorem, the TPS loss aims to reduce the triangle to a line segment, thereby maximizing the feature overlaps between X, Y, and Z. <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"508\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/1702096701351-1024x508.png\" alt=\"\" class=\"wp-image-88\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/1702096701351-1024x508.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/1702096701351-300x149.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/1702096701351-768x381.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/1702096701351.png 1090w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>SeNCE: Motivations<\/strong><\/p>\n\n\n\n<p>SeNCE outperforms PatchNCE and MoNCE in optimizing the amount of rain to produce realistic rainy images. The length of the arrow here represents the magnitude of the NCE losses. Suppose Z2 represent optimal amount of rain. For insufficient rain (Z1), SeNCE magnitude is small =&gt; GAN loss will defeat SeNCE loss, bringing image from Z1 to Z2. For excessive rain (Z3), SeNCE magnitude is large =&gt; SeNCE loss will defeat GAN loss, bringing image from Z3 to Z2. <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"510\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-3-1024x510.png\" alt=\"\" class=\"wp-image-89\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-3-1024x510.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-3-300x149.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-3-768x382.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-3.png 1278w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>SeNCE: Formulas<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"505\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-4-1024x505.png\" alt=\"\" class=\"wp-image-90\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-4-1024x505.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-4-300x148.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-4-768x379.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-4.png 1309w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Quantitative Comparison: Clear2Rainy<\/strong> <strong>(Rain Generation)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"520\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-5-1024x520.png\" alt=\"\" class=\"wp-image-91\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-5-1024x520.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-5-300x152.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-5-768x390.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-5.png 1258w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Quantitative Comparison: Rainy2Clear (Deraining)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"587\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-6-1024x587.png\" alt=\"\" class=\"wp-image-92\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-6-1024x587.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-6-300x172.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-6-768x440.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-6.png 1200w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Quantitative Comparison: Object Detection in Rain<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"590\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-7-1024x590.png\" alt=\"\" class=\"wp-image-93\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-7-1024x590.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-7-300x173.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-7-768x443.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-7.png 1161w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Qualitative Comparison: Clear2Rainy<\/strong> <strong>(Rain Generation)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"720\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-13-1024x720.png\" alt=\"\" class=\"wp-image-99\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-13-1024x720.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-13-300x211.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-13-768x540.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-13.png 1111w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Qualitative Comparison: Rainy2Clear (Deraining)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"786\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-14.png\" alt=\"\" class=\"wp-image-100\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-14.png 1000w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-14-300x236.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-14-768x604.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Qualitative Comparison: <strong>Object Detection in Rain<\/strong><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"997\" height=\"780\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-15.png\" alt=\"\" class=\"wp-image-101\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-15.png 997w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-15-300x235.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-15-768x601.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Qualitative Comparison: Day2Night<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"997\" height=\"801\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-16.png\" alt=\"\" class=\"wp-image-102\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-16.png 997w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-16-300x241.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-16-768x617.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Qualitative Comparison: Clear2Snowy<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"675\" height=\"805\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-17.png\" alt=\"\" class=\"wp-image-103\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-17.png 675w, https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image-17-252x300.png 252w\" sizes=\"auto, (max-width: 675px) 100vw, 675px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>References<\/strong><\/p>\n\n\n\n<p>[1]  Hu, Xueqi, et al. &#8220;Qs-attn: Query-selected attention for contrastive learning in i2i translation.&#8221;\u00a0<em>Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition<\/em>. 2022.<\/p>\n\n\n\n<p>[2] Huang, Xun, et al. &#8220;Multimodal unsupervised image-to-image translation.&#8221;\u00a0<em>Proceedings of the European conference on computer vision (ECCV)<\/em>. 2018.<\/p>\n\n\n\n<p>[3]  Lin, Tsung-Yi, et al. &#8220;Microsoft coco: Common objects in context.&#8221;\u00a0<em>Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13<\/em>. Springer International Publishing, 2014.<\/p>\n\n\n\n<p>[4] Liu, Ming-Yu, Thomas Breuel, and Jan Kautz. &#8220;Unsupervised image-to-image translation networks.&#8221;\u00a0<em>Advances in neural information processing systems<\/em>\u00a030 (2017).<\/p>\n\n\n\n<p>[5] Park, Taesung, et al. &#8220;Contrastive learning for unpaired image-to-image translation.&#8221;\u00a0<em>Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IX 16<\/em>. Springer International Publishing, 2020.<\/p>\n\n\n\n<p>[6] Redmon, Joseph, and Ali Farhadi. &#8220;Yolov3: An incremental improvement.&#8221;\u00a0<em>arXiv preprint arXiv:1804.02767<\/em>\u00a0(2018).<\/p>\n\n\n\n<p>[7] Yang, Wenhan, et al. &#8220;Deep joint rain detection and removal from a single image.&#8221;\u00a0<em>Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. 2017.<\/p>\n\n\n\n<p>[8] Zhan, Fangneng, et al. &#8220;Modulated contrast for versatile image synthesis.&#8221;\u00a0<em>Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition<\/em>. 2022.<\/p>\n\n\n\n<p>[9] Zheng, Shen, et al. &#8220;SAPNet: Segmentation-aware progressive network for perceptual contrastive deraining.&#8221;\u00a0<em>Proceedings of the IEEE\/CVF winter conference on applications of computer vision<\/em>. 2022.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this page, we briefly introduce our work in Spring 2023. Title TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain Links [Paper] [Code] [Slides] [Videos] Abstract Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/spring-2023\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Spring 2023&#8221;<\/span><\/a><\/p>\n","protected":false},"author":187,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-83","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Spring 2023 - Driving in bad weather<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/spring-2023\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Spring 2023 - Driving in bad weather\" \/>\n<meta property=\"og:description\" content=\"In this page, we briefly introduce our work in Spring 2023. Title TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain Links [Paper] [Code] [Slides] [Videos] Abstract Rain generation algorithms have the potential to improve the generalization of deraining methods and scene understanding in rainy conditions. However, in practice, they produce &hellip; Continue reading &quot;Spring 2023&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/spring-2023\/\" \/>\n<meta property=\"og:site_name\" content=\"Driving in bad weather\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-09T05:54:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/wp-content\/uploads\/sites\/97\/2023\/12\/image.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/\",\"name\":\"Spring 2023 - Driving in bad weather\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/wp-content\\\/uploads\\\/sites\\\/97\\\/2023\\\/12\\\/image.png\",\"datePublished\":\"2023-12-09T04:59:39+00:00\",\"dateModified\":\"2023-12-09T05:54:22+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/#primaryimage\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/wp-content\\\/uploads\\\/sites\\\/97\\\/2023\\\/12\\\/image.png\",\"contentUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/wp-content\\\/uploads\\\/sites\\\/97\\\/2023\\\/12\\\/image.png\",\"width\":873,\"height\":778},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/spring-2023\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Spring 2023\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/#website\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/\",\"name\":\"Driving in bad weather\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team20\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Spring 2023 - Driving in bad weather","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/mscvprojects.ri.cmu.edu\/f23team20\/spring-2023\/","og_locale":"en_US","og_type":"article","og_title":"Spring 2023 - Driving in bad weather","og_description":"In this page, we briefly introduce our work in Spring 2023. 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