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Claim this profileWorks199 from public data
- Image Super-Resolution Using Very Deep Residual Channel Attention Networks5,701
This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.
- Residual Dense Network for Image Super-Resolution4,163
This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
- Siamese Box Adaptive Network for Visual Tracking991
SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN, making SiamB Ban more flexible and general.
- Residual Dense Network for Image Restoration895
This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.
- A Comprehensive Survey of Vision-Based Human Action Recognition Methods499
This survey paper provides a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods.
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- Deep Network Cascade for Image Super-resolution264
Experiments on image super-resolution demonstrate that the proposed DNC can gradually upscale a low-resolution image with the increase of network layers and achieve more promising results in visual quality as well as quantitative performance.
- Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers230
This work proposes an adaptive tracker with spatio-temporal transformers (named AQA-Track), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components, and designs a novel attention mechanism for the interaction of existing queries to generate a new query in current frame.
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- What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation150
A new unsupervised semantic transfer model including two complementary modules for endoscopic lesions segmentation, which can alternatively determine where and how to explore transferable domain-invariant knowledge between labeled source lesions dataset and unlabeled target diseases dataset is developed.
- Toward Unified Token Learning for Vision-Language Tracking136
A simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task, and the design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling.
- Learning to Filter: Siamese Relation Network for Robust Tracking134
A novel Siamese relation network is proposed, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM), which performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into theSiamese framework to generate accurate tracking result.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System123
A new tracking system, which aims to combine the information from RGB and infrared modalities for object tracking, based on the proposed machine learning model that can alleviate the modality discrepancy issue under the proposed modality consistency constraint.
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- CNNTracker: Online discriminative object tracking via deep convolutional neural network104
A deep learning architecture to learn the most discriminative features dynamically via a convolutional neural network (CNN) to enhance the discrim inative ability of the appearance model in three-fold is proposed.
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- Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services97
A fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust is proposed.
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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 2026-10-14. 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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