Search Results for author:
Found 37 papers, 12 papers with code
MaGIC: Multi-modality Guided Image Completion
In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e. g., text, canny edge, sketch, segmentation, reference image, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i. e., arbitrary multi-modality) for image completion.
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
Current arbitrary style transfer models are limited to either image or video domains.
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework.
CCTV-Gun: Benchmarking Handgun Detection in CCTV Images
In this paper, we present a meticulously crafted and annotated benchmark, called \textbf{CCTV-Gun}, which addresses the challenges of detecting handguns in real-world CCTV images.
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack.
Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment
To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning.
PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection
Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion.
High-Fidelity Image Inpainting with GAN Inversion
Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space.
Divert More Attention to Vision-Language Tracking
By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer.
AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild
Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation.
Learning Target-aware Representation for Visual Tracking via Informative Interactions
The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced by our extensive experiments on multiple benchmarks.
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
The potential of Transformer in representation learning remains under-explored.
Ranked #6 on
Visual Object Tracking
on TrackingNet
Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism
The aim of this study was to investigate whether a deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking
The key is to bridge box regression and classification via an alignment step, which leads to more accurate features for proposal classification with improved robustness.
Transparent Object Tracking Benchmark
To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Detection and Tracking Meet Drones Challenge
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.
Semantic-Aware Label Placement for Augmented Reality in Street View
Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically.
ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network
In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images.
Experimental demonstration of entanglement-enabled universal quantum cloning in a circuit
No-cloning theorem forbids perfect cloning of an unknown quantum state.
Quantum Physics
Clustered Object Detection in Aerial Images
The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet).
Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment
To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
C-RPN is trained end-to-end with the multi-task loss function.
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units.
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.
Graph Correspondence Transfer for Person Re-identification
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification
Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation.
Parallel Tracking and Verifying
Being intensively studied, visual object tracking has witnessed great advances in either speed (e. g., with correlation filters) or accuracy (e. g., with deep features).
Dense Recurrent Neural Networks for Scene Labeling
Recently recurrent neural networks (RNNs) have demonstrated the ability to improve scene labeling through capturing long-range dependencies among image units.
Scalable Quantum Tomography with Fidelity Estimation
We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states.
Quantum Physics
Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence.
Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
In this paper we study the problem from a new perspective and present a novel parallel tracking and verifying (PTAV) framework, by taking advantage of the ubiquity of multi-thread techniques and borrowing from the success of parallel tracking and mapping in visual SLAM.
SANet: Structure-Aware Network for Visual Tracking
Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction.
Multi-level Contextual RNNs with Attention Model for Scene Labeling
Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored.
