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Works239 from public data
- 1,955
- Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring663
A deep {hierarchical multi-patch network} inspired by Spatial Pyramid Matching to deal with blurry images via a fine-to-coarse hierarchical representation and is the first real-time deep motion deblurring model for 720p images at 30fps.
- Rotation Averaging650
This paper is conceived as a tutorial on rotation averaging, summarizing the research that has been carried out in this area and contains many new results, which were developed to fill gaps in knowledge, answering fundamental questions such as radius of convergence of the algorithms, and existence of local minima.
- 647
- Simultaneously Localize, Segment and Rank the Camouflaged Objects599
It is argued that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques.
- IoU Loss for 2D/3D Object Detection492
By integrating the implemented IoU loss into several state-of-the-art 3D object detectors, consistent improvements have been achieved for both bird-eye-view 2D detection and point cloud 3D detection on the public KITTI [3] benchmark.
- Hierarchical Neural Architecture Search for Deep Stereo Matching438
This paper proposes the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework and optimizing the architectures of the entire pipeline jointly.
- CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching406
This paper proposes CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network, and employs a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space.
- UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders396
Inspired by the saliency data labeling process, a probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space is proposed.
- 373
- Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera341
This paper proposes a simple and effective approach, the Event-based Double Integral (EDI) model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data, based on solving a simple non-convex optimization problem in a single scalar variable.
- Weakly-Supervised Salient Object Detection via Scribble Annotations320
This paper proposes a weakly-supervised salient object detection model to learn saliency from scribble annotations, and presents a new metric, termed saliency structure measure, as a complementary metric to evaluate sharpness of the prediction.
- Uncertainty-aware Joint Salient Object and Camouflaged Object Detection308
This paper proposes a paradigm of lever-aging the contradictory information to enhance the detection ability of both salient object detection and camouflaged object detection, and introduces a "similarity measure" module to explicitly model the contradicting attributes of these two tasks.
- Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN260
An image classification based approach to large scale action recognition from 3D skeleton videos is presented and a multi-scale deep convolutional neural network (CNN) is proposed for the image classification task, which could enhance the temporal frequency adjustment of the model.
- ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving186
This paper contributes the first large scale database suitable for 3D car instance understanding – ApolloCar3D and builds various baseline algorithms with the state-of-the-art deep convolutional neural networks.
- 181
- Complementary Patch for Weakly Supervised Semantic Segmentation173
A novel Complementary Patch (CP) Representation is proposed and it is proved that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts is greater than or equal to theInformation of the baseline CAM.
- Uncertainty Inspired RGB-D Saliency Detection168
Qualitative and quantitative results on six challengingRGB-D benchmark datasets show the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process superior performance in learning the distribution of saliency maps.
- Deep learning based point cloud registration: an overview157
This paper summarizes recent progress and presents a comprehensive overview of popular deep learning based point cloud registration methods, and classify the popular approaches into different categories such as, correspondences-based or correspondence-free, effective modules: feature extractor, matching, outlier rejection, and motion estimation.
- Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference145
A deep end-to-end learning framework to tackle monocular depth estimation, which learns the direct mapping from a color image to the corresponding depth map, and utilizes soft-weighted-sum inference instead of the hard-max inference, transforming the discretized depth score to continuous depth value.
- 3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis143
This paper proposes a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis that achieves 3D geometry-aware deformation modeling, which enables improved dynamic view synthesis and 3D dynamic reconstruction.
- RGB-D Saliency Detection via Cascaded Mutual Information Minimization131
This paper introduces a novel multistage cascaded learning framework via mutual information minimization to explicitly model the multi-modal information between RGB image and depth data.
- 130
- PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching125
The proposed PCW-Net is a Pyramid Combination and Warping cost volume-based network to achieve good performance on both cross-domain generalization and stereo matching accuracy on various benchmarks, and shows strong cross- domaingeneralization and outperforms existing state-of-the-arts with a large margin.
- 124
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 2026-10-13. 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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