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Works221 from public data
- Binary neural networks: A survey597
A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.
- DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization383
DNGaussian is introduced, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs, and a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance.
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- Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection257
This work devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data and outperforms state-of-the-art methods that require no labeled training data.
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- Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss230
This work introduces a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences and a bounded penalization of large differences.
- Deep learning-based 3D point cloud classification: A systematic survey and outlook217
The purpose of this paper is to provide researchers in this field with the latest research progress and future trends, and to summarize deep learning-based methods for point cloud classification and complement recent research work.
- Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching212
This paper proposes to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities and achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks.
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- Goal-Oriented Gaze Estimation for Zero-Shot Learning171
This work introduces a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization based on the class-level attributes for ZSL and implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks.
- Graph characteristics from the heat kernel trace159
This paper explores how permutation invariants computed from the trace of the heat kernel can be used to characterize graphs for the purposes of measuring similarity and clustering, and compares them with the use of the Laplacian spectrum.
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- SHREC'12 Track: Generic 3D Shape Retrieval96
The aim of this track is to measure and compare the performance of generic 3D shape retrieval methods implemented by different participants over the world and their retrieval accuracies were evaluated using 7 commonly used performance metrics.
- Material based salient object detection from hyperspectral images93
A material-based salient object detection method which can effectively distinguish objects with similar perceived color but different spectral responses, and outperforms several existing hyperspectral salient object Detection approaches and the state-of-the-art methods proposed for RGB images.
- Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective91
A simple pixel-wise contrastive learning across the viewpoints for stereo matching performance to unseen domains is proposed, which achieves superior performance over several state-of-the-art networks.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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