{"id":13234,"date":"2025-06-05T08:37:40","date_gmt":"2025-06-05T08:37:40","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-enhancing-entertainment-translation-for-indian-languages-using-adaptive-context-style-and-llms-copy\/"},"modified":"2025-06-06T06:01:10","modified_gmt":"2025-06-06T06:01:10","slug":"summarizing-precise-event-spotting-in-sports-videos-solving-long-range-dependency-and-class-imbalance","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-precise-event-spotting-in-sports-videos-solving-long-range-dependency-and-class-imbalance\/","title":{"rendered":"Summarizing \u2018Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance\u2019"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"13234\" class=\"elementor elementor-13234\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cd44eb5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cd44eb5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f11b70\" data-id=\"9f11b70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-215a70e elementor-widget elementor-widget-heading\" data-id=\"215a70e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">BLOGS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-28dc161 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"28dc161\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-63cf269\" data-id=\"63cf269\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6837436 elementor-widget elementor-widget-heading\" data-id=\"6837436\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Summarizing \u2018Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance\u2019<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bd1630 elementor-widget elementor-widget-text-editor\" data-id=\"9bd1630\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Sanchayan Santra, Vishal Chudasama, Pankaj. W, Vineeth N Balasubramanian<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a034cb elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"7a034cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>30<sup>th<\/sup> September 2024<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7d1e72 elementor-widget elementor-widget-image\" data-id=\"a7d1e72\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"750\" height=\"361\" src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-1-768x370.png\" class=\"attachment-medium_large size-medium_large wp-image-13237\" alt=\"\" srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-1-768x370.png 768w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-1-300x144.png 300w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-1.png 910w\" sizes=\"(max-width: 750px) 100vw, 750px\" style=\"width:100%;height:48.13%;max-width:910px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ed0fea elementor-widget elementor-widget-text-editor\" data-id=\"7ed0fea\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Fig. 1: Overview of proposed framework<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9202657 elementor-widget elementor-widget-text-editor\" data-id=\"9202657\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The framework composed of a spatio-temporal feature extractor and a temporal block for capturing long-range dependency before the classifier. In each bottleneck block we add ASTRM after the first conv. ASTRM further enhances the features with local spatial, local temporal and global temporal information. The network is trained with Soft-IC loss in addition to the classification loss to handle class imbalance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-07c56c7 elementor-widget elementor-widget-text-editor\" data-id=\"07c56c7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Vishal Chudasama summarises paper titled Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance co-authored by Sanchayan Santra, Pankaj. W, Vineeth N Balasubramanian accepted at the <strong><a href=\"https:\/\/cvpr.thecvf.com\/Conferences\/2025\/ProgramOverview\" target=\"_blank\" rel=\"noopener\">CVPR 2025 | June 2025<\/a><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0a3e28 elementor-widget elementor-widget-text-editor\" data-id=\"f0a3e28\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Introduction: <\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d95d9a3 elementor-widget elementor-widget-text-editor\" data-id=\"d95d9a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Precise Event Spotting methods aim to predict an event at the exact frame it occurs (e.g., goal, boundary). This is crucial for generating game summaries, finding specific events, and editing lengthy matches. However, it poses significant challenges and require a deep understanding of the game and its key events. Existing PES methods mainly face two challenges. First is the long-range temporal dependency that arises due to the nature of events. To predict the event frames, observing just the nearby frames does not suffice; several other potentially distant frames need to be observed to predict the event correctly. Second, there is an inherent imbalance in the number of samples among the classes. Some events naturally occur more frequently than others. Existing methods prefers features extracted from pretrained networks and transformer-based temporal modules for the prediction. But transformers does not work well with long videos. On the other hand, the issue of sample imbalance among classes has not been addressed properly in the literature.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1246231 elementor-widget elementor-widget-text-editor\" data-id=\"1246231\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>To address these issues, we introduce three-way approach:<\/strong><\/p><ul><li>We proposed an end-to-end framework using a CNN-based spatio-temporal feature extractor and GRU-based temporal network to achieve precise event spotting while addressing long-range dependency and class imbalance.\u00a0<\/li><li>In the spatio-temporal feature extractor, we introduce Adaptive Spatio-Temporal Refinement Module (<strong>ASTRM<\/strong>), which enhances the features with spatial and temporal information.\u00a0<\/li><li>We also introduce Soft Instance Contrastive (<strong>Soft-IC<\/strong>) loss to enforce compact features and improve class separability, addressing the class imbalance issue.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-424bb7e elementor-widget elementor-widget-text-editor\" data-id=\"424bb7e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Key Results:<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd9e70e elementor-widget elementor-widget-text-editor\" data-id=\"cd9e70e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To validate our proposed model, we evaluated on various sports benchmark datasets i.e., SoccerNet-V2, Tennis, FineGym, Figure Skating and non-sports dataset such as Fine Action.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-078047f elementor-widget elementor-widget-text-editor\" data-id=\"078047f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Table 1<\/strong>: Result Comparison on SoccerNet-V2 datasets in terms of mAP tight and loose settings. Best- and second best-performing measures are highlighted with <strong>bold font<\/strong> and <em><u>underline font,<\/u><\/em> respectively<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-817c0e6 elementor-widget elementor-widget-text-editor\" data-id=\"817c0e6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<table><tbody><tr><td width=\"185\"><p>Model<\/p><\/td><td width=\"89\"><p>Params (M)<\/p><\/td><td width=\"119\"><p>GFLOPs<\/p><\/td><td width=\"121\"><p>mAP-Tight<\/p><\/td><td width=\"110\"><p>mAP-Loose<\/p><\/td><\/tr><tr><td width=\"185\"><p>Baidu (TF)<\/p><\/td><td width=\"89\"><p>&#8212;<\/p><\/td><td width=\"119\"><p>&#8212;<\/p><\/td><td width=\"121\"><p>47.05<\/p><\/td><td width=\"110\"><p>73.77<\/p><\/td><\/tr><tr><td width=\"185\"><p>SpotFormer<\/p><\/td><td width=\"89\"><p>&#8212;<\/p><\/td><td width=\"119\"><p>&#8212;<\/p><\/td><td width=\"121\"><p>60.9<\/p><\/td><td width=\"110\"><p>76.1<\/p><\/td><\/tr><tr><td width=\"185\"><p>E2E-Spot (RegNet-Y 200MF)<\/p><\/td><td width=\"89\"><p>4.46<\/p><\/td><td width=\"119\"><p>39.61<\/p><\/td><td width=\"121\"><p>61.19<\/p><\/td><td width=\"110\"><p>73.25<\/p><\/td><\/tr><tr><td width=\"185\"><p>E2E-Spot (RegNet-Y 800MF)<\/p><\/td><td width=\"89\"><p>12.64<\/p><\/td><td width=\"119\"><p>151.4<\/p><\/td><td width=\"121\"><p>61.82<\/p><\/td><td width=\"110\"><p>74.05<\/p><\/td><\/tr><tr><td width=\"185\"><p>Spivak<\/p><\/td><td width=\"89\"><p>17.46<\/p><\/td><td width=\"119\"><p>461.89<\/p><\/td><td width=\"121\"><p>65.1<\/p><\/td><td width=\"110\"><p><u>78.50<\/u><\/p><\/td><\/tr><tr><td width=\"185\"><p>ASTRA<\/p><\/td><td width=\"89\"><p>44.33<\/p><\/td><td width=\"119\"><p>8.83<\/p><\/td><td width=\"121\"><p>66.63<\/p><\/td><td width=\"110\"><p>78.14<\/p><\/td><\/tr><tr><td width=\"185\"><p>T-DEED (RegNet-Y 200MF)<\/p><\/td><td width=\"89\"><p>16.36<\/p><\/td><td width=\"119\"><p>21.96<\/p><\/td><td width=\"121\"><p>39.43<\/p><\/td><td width=\"110\"><p>52.93<\/p><\/td><\/tr><tr><td width=\"185\"><p>T-DEED (RegNet-Y 800MF)<\/p><\/td><td width=\"89\"><p>46.22<\/p><\/td><td width=\"119\"><p>60.25<\/p><\/td><td width=\"121\"><p>21.57<\/p><\/td><td width=\"110\"><p>30.49<\/p><\/td><\/tr><tr><td width=\"185\"><p>UGLF<\/p><\/td><td width=\"89\"><p>4.46<\/p><\/td><td width=\"119\"><p>39.61<\/p><\/td><td width=\"121\"><p>63.51<\/p><\/td><td width=\"110\"><p>73.98<\/p><\/td><\/tr><tr><td width=\"185\"><p>COMEDIAN (ViSwin-T)<\/p><\/td><td width=\"89\"><p>70.12<\/p><\/td><td width=\"119\"><p>222.76<\/p><\/td><td width=\"121\"><p><em><u>71.33<\/u><\/em><\/p><\/td><td width=\"110\"><p>77.07<\/p><\/td><\/tr><tr><td width=\"185\"><p><strong>Proposed<\/strong><\/p><\/td><td width=\"89\"><p>6.46<\/p><\/td><td width=\"119\"><p>60.25<\/p><\/td><td width=\"121\"><p><strong>73.74<\/strong><\/p><\/td><td width=\"110\"><p><strong>79.11<\/strong><\/p><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8981a35 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"8981a35\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"447\" height=\"260\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-2.png\" class=\"attachment-full size-full wp-image-13238 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-2.png 447w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-2-300x174.png 300w\" data-sizes=\"(max-width: 447px) 100vw, 447px\" style=\"--smush-placeholder-width: 447px; --smush-placeholder-aspect-ratio: 447\/260;width:100%;height:58.17%;max-width:447px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 2: Per class score analysis on a few classes of SoccerNetV2 dataset in tight setting. <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-edc2ca0 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"edc2ca0\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"501\" height=\"216\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-3.png\" class=\"attachment-full size-full wp-image-13239 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-3.png 501w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-3-300x129.png 300w\" data-sizes=\"(max-width: 501px) 100vw, 501px\" style=\"--smush-placeholder-width: 501px; --smush-placeholder-aspect-ratio: 501\/216;width:100%;height:43.11%;max-width:501px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 3: Per-class score comparison on \u03b4=0 setting in terms of mAP on the Tennis dataset. <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a64086 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"4a64086\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"491\" height=\"209\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-4.png\" class=\"attachment-full size-full wp-image-13240 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-4.png 491w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-4-300x128.png 300w\" data-sizes=\"(max-width: 491px) 100vw, 491px\" style=\"--smush-placeholder-width: 491px; --smush-placeholder-aspect-ratio: 491\/209;width:100%;height:42.57%;max-width:491px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 4: Per-class score comparison on \u03b4=0 setting in terms of mAP on the Figure Skating dataset. <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b564932 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"b564932\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"454\" height=\"204\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-5.png\" class=\"attachment-full size-full wp-image-13241 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-5.png 454w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/06\/blog-05-06-2025-5-300x135.png 300w\" data-sizes=\"(max-width: 454px) 100vw, 454px\" style=\"--smush-placeholder-width: 454px; --smush-placeholder-aspect-ratio: 454\/204;width:100%;height:44.93%;max-width:454px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Fig. 5: Per-class score comparison on \u03b4=0 setting in terms of mAP on the FineGym dataset. <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5416e1 elementor-widget elementor-widget-text-editor\" data-id=\"d5416e1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Conclusion<\/strong>:<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9aff2cc elementor-widget elementor-widget-text-editor\" data-id=\"9aff2cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This paper proposes an end-to-end network for precise spotting of events in sports videos. It effectively tackles the long-range dependency and class imbalance issues and outperforms existing methods. To this extent, we introduce a new ASTRM module for enhancing spatio-temporal features. Further, to address the class imbalance issue, we propose Soft-IC loss, which enforces compact features and improves class separability. We further employed ASAM during training to ensure the model performs well on unseen data. While our method has advanced the state-of-the-art, especially in the more challenging cases with a relatively simple design, there is further scope for improvement on the two fronts we tried to address. Certain larger networks can improve the performance further but at the cost of computation. On the other hand, the spatial and temporal resolution at which the data is being processed will likely have an impact. The precise spotting may become easier with increased resolution, but it increases the processing burden.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67d4f0b elementor-widget elementor-widget-text-editor\" data-id=\"67d4f0b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page:\u00a0<a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\">Open Innovation with Sony R&amp;D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0362925 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"0362925\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In most of the cases, it has been found that Content Driven sessions outperform the time driven sessions. The results are obtained on 6 baselines: STAMP, NARM, GRU4Rec, CD-HRNN, Tr4Rec on datasets like Movielens (Movies), GoodRead Book, LastFM (Music), Amazon (e-commerce).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c0518a1 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c0518a1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b15be70\" data-id=\"b15be70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-55dd72b\" data-id=\"55dd72b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e06d72d elementor-widget elementor-widget-image\" data-id=\"e06d72d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"322\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png\" class=\"attachment-full size-full wp-image-11786 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png 512w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2-300x189.png 300w\" data-sizes=\"(max-width: 512px) 100vw, 512px\" style=\"--smush-placeholder-width: 512px; --smush-placeholder-aspect-ratio: 512\/322;width:100%;height:62.89%;max-width:512px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-fd52b32\" data-id=\"fd52b32\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b69060 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b69060\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cfbe302\" data-id=\"cfbe302\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d045fb elementor-widget elementor-widget-text-editor\" data-id=\"6d045fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe introduced modules and techniques help the proposed method to align known class\nrepresentations effectively so that it can detect the unknown objects accurately. To validate\nthis, we carried out extensive experiments &#038; ablation studies and found that the proposed\nmethod outperforms existing SOTA methods with significant improvement on the MS-COCO\n&#038; PASCAL VOC dataset for the OSOD task.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f97c4c4 elementor-widget elementor-widget-text-editor\" data-id=\"f97c4c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about the paper, visit: <a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2024\/papers\/Sarkar_Open-Set_Object_Detection_by_Aligning_Known_Class_Representations_WACV_2024_paper.pdf\" target=\"_blank\" rel=\"noopener\">Open-Set Object Detection by Aligning Known Class\nRepresentations (thecvf.com)<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e2f9cc elementor-widget elementor-widget-text-editor\" data-id=\"9e2f9cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019\nsection on our \u2018Open Innovation\u2019s page: <a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\" target=\"_blank\" rel=\"noopener\">Open Innovation with Sony R&amp;D \u2013 Sony Research\nIndia<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The framework composed of a spatio-temporal&#8230; <\/p>\n","protected":false},"author":1,"featured_media":13246,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-13234","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n<title>Summarizing \u2018Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance\u2019 - Sony Research India<\/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:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-precise-event-spotting-in-sports-videos-solving-long-range-dependency-and-class-imbalance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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