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- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration1,170
This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the inline-formula notation, and derives novel upper and lower bounds for the registration error function.
- Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set910
A novel deep 3D face reconstruction approach that leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation is proposed.
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- Learning Human Interaction by Interactive Phrases140
A novel hierarchical model to encode interactive phrases based on the latent SVM framework where interactive phrases are treated as latent variables is proposed to deal with motion ambiguity and partial occlusion in interactions.
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- Scene-Aware Context Reasoning for Unsupervised Abnormal Event Detection in Videos124
A scene-aware context reasoning method that exploits context information from visual features for unsupervised abnormal event detection in videos, which bridges the semantic gap between visual context and the meaning of abnormal events is proposed.
- Overcoming Language Priors in VQA via Decomposed Linguistic Representations120
A novel method of language attention-based VQA that learns decomposed linguistic representations of questions and utilizes the representations to infer answers for overcoming language priors is presented.
- A Hyperbolic-to-Hyperbolic Graph Convolutional Network111
A manifold-preserving graph convolution that consists of ahyperbolic feature transformation and a hyperbolic neighborhood aggregation and achieves substantial improvements on the link prediction, node classification, and graph classification tasks.
- Discriminative human action recognition in the learned hierarchical manifold space104
This paper proposes a hierarchical discriminative approach for human action recognition that consists of feature extraction with mutual motion pattern analysis and discrim inative action modeling in the hierarchical manifold space.
- NON-NEGATIVE MATRIX FACTORIZATION FRAMEWORK FOR FACE RECOGNITION103
A framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results is proposed and two novel subspace methods are presented.
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- Intrinsic Image Decomposition Using Optimization and User Scribbles91
This paper presents a novel high-quality intrinsic image recovery approach using optimization and user scribbles that achieves a better recovery result of intrinsic reflectance and illumination components than the previous approaches.
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- Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model78
This paper presents an automatic LV myocardial boundary segmentation method using the parametric active contour model (or snake model), and a novel shape-similarity based energy is proposed to prevent the snake contour from being strapped in faulty edges and to preserve weak boundaries.
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- A deep Coarse-to-Fine network for head pose estimation from synthetic data76
This paper designs a rendering pipeline to synthesize realistic head images and generates an annotated dataset with a collection of 310k head poses, and proposes a deep neural network following the Coarse-to-Fine strategy to estimate head poses.
- Temporal dynamic appearance modeling for online multi-person tracking75
This work proposes a temporal dynamic appearance model for online multi-person tracking, and presents an incremental learning approach to learn the model online, which outperforms the state-of-the-art multi- person tracking algorithms.
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- View-Invariant Action Recognition Using Latent Kernelized Structural SVM70
A novel learning algorithm is proposed for the view-invariant action recognition, which extends the kernelized structural SVM framework to include latent variables and combines the low-level visual cue, mid-level correlation description, and high-level label information into a novel nonlinear kernel.
- A Decomposition Model for Stereo Matching69
A decomposition model for stereo matching is presented to solve the problem of excessive growth in computational cost as the resolution increases and achieves 10−100× speed increase while obtaining comparable disparity estimation results.
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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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