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Works465 from public data
- 2,959
- LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking1,655
LaSOT is presented, a high-quality benchmark for Large-scale Single Object Tracking that consists of 1,400 sequences with more than 3.5M frames in total, and is the largest, to the best of the authors' knowledge, densely annotated tracking benchmark.
- Detection and Tracking Meet Drones Challenge1,344
This work provides a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and concludes the challenge as well as propose future directions.
- Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection1,225
A novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), is proposed, Inspired by recent advances of deep learning in computer vision, for pavement crack detection that outperforms the state-of-the-art crack detection, edge detection, and semantic segmentation methods.
- 1,174
- 950
- 912
- M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network874
A powerful end-to-end one-stage object detector called M2Det is designed and train by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one- stage detectors.
- Salient Object Detection in the Deep Learning Era: An In-Depth Survey810
This paper reviews deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection, and looks into the generalization and difficulty of existing SOD datasets.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- 779
- 724
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection538
This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
- Robust Visual Tracking using 1 Minimization524
This paper proposes a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework and introduces a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure.
- 521
- 500
- Automatic thumbnail cropping and its effectiveness485
This work studies the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable, and evaluates automatic cropping techniques based on a general method that detects salient portions of images.
- Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking457
A multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in aSiamese network, which achieves state-of-the-art results and runs in real-time.
- Transfer Learning Based Visual Tracking with Gaussian Processes Regression454
This paper directly analyze this probability of target appearance as exponentially related to the confidence of a classifier output using Gaussian Processes Regression (GPR), and introduces a latent variable to assist the tracking decision.
- Clustered Object Detection in Aerial Images437
This paper proposes a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework and achieves promising performance in comparison with state-of-the-art detectors.
- LaSOT: A High-quality Large-scale Single Object Tracking Benchmark424
The goal in releasing LaSOT is to provide a dedicated high quality platform for both training and evaluation of trackers, and to take advantage of the close connection between visual appearance and natural language, the largest densely annotated tracking benchmark to be known.
- Human-Aware Motion Deblurring403
A human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG) humans based on a triple-branch encoder-decoder architecture that performs favorably against the state-of-the-art motion deblurred methods, especially in capturing semantic details.
- 378
- 371
- AutoFormer: Searching Transformers for Visual Recognition366
This work proposes a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search, which surpass the recent state-of-the-arts such as ViT and DeiT and achieves top-1 accuracy on ImageNet.
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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