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In nighttime conditions, high noise levels and bright Illumination sources degrade image quality, making low-light image enhancement challenging. Thermal images provide complementary information, offering richer textures and structural details. We propose RT-X Net, a cross-attention network that fuses RGB and thermal images for nighttime image enhancement. We leverage self-attention networks for feature extraction and a cross-attention mechanism for fusion to effectively integrate information from both modalities.
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- Read more about SUPPLEMENTARY MATERIALS FOR EVENT DENOISING BASED ON ITERATIVE TREE-STRUCTURED INFORMATION AGGREGATIO
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SUPPLEMENTARY MATERIALS FOR EVENT DENOISING BASED ON ITERATIVE
TREE-STRUCTURED INFORMATION AGGREGATIO
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- Read more about Supplement - CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION
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Supplement for the research paper CONTEXTLOSS: CONTEXT INFORMATION FOR TOPOLOGY-PRESERVING SEGMENTATION
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- Read more about Cross-Domain Video Object Detection via Augmented-Shot FineTuning-0
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This document contains supplementary material for the ICIP PAPER.
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- Read more about CROSS-DOMAIN VIDEO OBJECT DETECTION VIA AUGMENTED-SHOT FINETUNING
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This document contains supplementary material for the ICIP PAPER.
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- Read more about Video - 3DGS_result_from_16.40_and_17.27
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The video represents the Sheep-Sculpture rendering at 360 degrees of view by the original 3DGS method from a dataset that contains the 16:40 and 17:27 time intervals images.
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- Read more about Video - Ours_estimated_16.59_from_16.40_and_17.27
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The video represents the Sheep-Sculpture rendering at 16:59 from 360 degrees of view by our time-dependent modeling method from a dataset that contains the 16:40 and 17:27 time intervals images.
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- Read more about Supplementary - Towards Image Copy Detection at E-commerce Scale
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Copy Detection system aims to identify if a query image is an edited/manipulated copy of an image from a large reference database with millions of images. While global image descriptors can retrieve visually similar images, they struggle to differentiate near-duplicates from semantically similar instances. We propose a dual-triplet metric learning (DTML) technique to learn global image features that group near-duplicates closer than visually similar images while maintaining the semantic structure of the embedding space.
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- Read more about Supplementary Materials for ICIP2025
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Supplementary Materials for "RETHINKING IMAGE HISTOGRAM MATCHING FOR IMAGE CLASSIFICATION" at ICIP2025.
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- Read more about USER-IN-THE-LOOP VIEW SAMPLING WITH ERROR PEAKING VISUALIZATION
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Augmented reality (AR) provides ways to visualize missing view samples for novel view synthesis. Existing approaches present 3D annotations for new view samples and task users with taking images by aligning the AR display. This data collection task is known to be mentally demanding and limits capture areas to pre-defined small areas due to ideal but restrictive underlying sampling theory. To free users from 3D annotations and limited scene exploration, we propose using locally reconstructed light fields and visualizing errors to be removed by inserting new views.
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