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- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration1,193
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.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison698
This paper introduces a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers, and proposes a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose- based method.
- 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.
- Learning Trajectory Dependencies for Human Motion Prediction584
A simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints, and design a new graph convolutional network to learn graph connectivity automatically.
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- Deblurring by Realistic Blurring494
This paper proposes a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning- to-DeBlurGAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
- 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.
- Learning to Estimate Hidden Motions with Global Motion Aggregation436
This paper argues that the occlusion problem can be better solved in the two-frame case by modelling image self-similarities, and introduces a global motion aggregation module, a transformer-based approach to find long-range dependencies between pixels in the first image, and perform global aggregation on the corresponding motion features.
- Neural Aggregation Network for Video Face Recognition419
This NAN is trained with a standard classification or verification loss without any extra supervision signal, and it is found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces.
- Deep Image Deblurring: A Survey415
This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review.
- Lending Orientation to Neural Networks for Cross-View Geo-Localization406
A novel method which endows deep neural networks with the `commonsense' of orientation is proposed, given a ground-level spherical panoramic image as query input and a Siamese network which explicitly encodes the orientation of each pixel of the images.
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- Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization341
A new deep network is developed to explicitly address inherent differences between ground and aerial views, and introduces a feature aggregation strategy via learning multiple spatial embeddings to improve the robustness of feature representation.
- Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices316
To encode the geometry of the manifold in the mapping, a family of provably positive definite kernels on the Riemannian manifold of SPD matrices is introduced, derived from the Gaussian kernel, but exploit different metrics on the manifold.
- MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing291
A new Transformer variant, named Multi-branch Transformer expanded by Taylor formula (MB-TaylorFormer), which can em-bed coarse to fine features more flexibly at the patch embedding stage and capture long-distance pixel interactions with limited computational cost.
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- MB-TaylorFormer V2: Improved Multi-Branch Linear Transformer Expanded by Taylor Formula for Image Restoration277
The proposed model, named the second version of Taylor formula expansion-based Transformer (for short MB-TaylorFormer V2) has the capability to concurrently process coarse-to-fine features, capture long-distance pixel interactions with limited computational cost, and improve the approximation of the Taylor expansion remainder.
- Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching271
A Dynamic Similarity Matching network is designed to estimate cross-view orientation alignment during localization and improves state-of-the-art performance on large-scale geo-localization datasets.
- Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels270
It is shown that many popular algorithms designed for Euclidean spaces, such as support vector machines, discriminant analysis and principal component analysis can be generalized to Riemannian manifolds with the help of such positive definite Gaussian kernels.
- GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions240
The GridFormer framework achieves state-of-the-art results on five diverse image restoration tasks in adverse weather conditions, including image deraining, dehazing, deraining & dehazing, desnowing, and multi-weather restoration.
- Align and Prompt: Video-and-Language Pre-training with Entity Prompts238
This paper proposes Align and Prompt: a new video-and-language pre-training framework (AlPro), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors.
- Optimal Feature Transport for Cross-View Image Geo-Localization234
A novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images, and is implemented as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison.
- UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability224
This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm, and compares its result to state-of-the-art minimal, non-minimal, central, and non-central PNP algorithms, and demonstrates universal applicability, competitive noise resilience, and superior computational efficiency.
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 2026-10-12. 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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