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- Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks151
A comprehensive survey of existing DL-based methods for event-based vision is provided by structurally grouping them into two major categories: 1) image/video reconstruction and restoration; 2) event-based scene understanding and 3D vision.
- Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentation51
This paper proposes a novel UDA framework that can effectively address the distortion problems for panoramic semantic segmentation and proposes distortion-aware attention (DA) capturing the neighboring pixel distribution without using any geometric constraints.
- SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events27
A novel Spatial Reliability-oriented Fusion Network (SRFNet), that can estimate depth with fine-grained structure at both daytime and nighttime and outperforms the prior methods, e.g., RAMNet [6], especially in night scenes.
- Leader360V: A Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment12
Leader360V is introduced, the first large-scale, labeled real-world 360 video datasets for instance segmentation and tracking, and an automatic labeling pipeline, which subtly coordinates pre-trained 2D segmentors and large language models to facilitate the labeling.
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- Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction1
This work introduces a Distractor-aware Training (DAT) strategy that separates clean features from distractor-contaminated ones in the attention mechanism while enforcing feature consistency across images, and proposes Visual Geometry Transformer in the Wild (VGTW), an end-to-end framework for robust reconstruction from inconsistent views.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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