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Found 56 papers, 27 papers with code
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.
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.
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.
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.
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
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.
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).
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.
Graph Correspondence Transfer for Person Re-identification
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
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.
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.
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.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
C-RPN is trained end-to-end with the multi-task loss function.
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.
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).
Experimental demonstration of entanglement-enabled universal quantum cloning in a circuit
No-cloning theorem forbids perfect cloning of an unknown quantum state.
Quantum Physics
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.
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.
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.
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.
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.
Transparent Object Tracking Benchmark
To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking.
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.
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.
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.
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
The potential of Transformer in representation learning remains under-explored.
Ranked #9 on
Visual Object Tracking
on TrackingNet
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i. e., arbitrary multi-modality) for image completion.
Deficiency-Aware Masked Transformer for Video Inpainting
Firstly, we pretrain a image inpainting model DMT_img serve as a prior for distilling the video model DMT_vid, thereby benefiting the hallucination of deficiency cases.
Ranked #1 on
Video Inpainting
on DAVIS
Divert More Attention to Vision-Language Object Tracking
To thoroughly evidence the effectiveness of our method, we integrate the proposed framework on three tracking methods with different designs, i. e., the CNN-based SiamCAR, the Transformer-based OSTrack, and the hybrid structure TransT.
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes.
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection
Effective feature fusion of multispectral images plays a crucial role in multi-spectral object detection.
Ranked #2 on
Object Detection
on VEDAI
Unsupervised Domain Adaptive Detection with Network Stability Analysis
In this work, drawing inspiration from the concept of stability from the control theory that a robust system requires to remain consistent both externally and internally regardless of disturbances, we propose a novel framework that achieves unsupervised domain adaptive detection through stability analysis.
Collaborative Three-Stream Transformers for Video Captioning
Meanwhile, we propose a cross-granularity attention module to align the interactions modeled by the three branches of transformers, then the three branches of transformers can support each other to exploit the most discriminative semantic information of different granularities for accurate predictions of captions.
Accurate and Fast Compressed Video Captioning
Addressing this, we study video captioning from a different perspective in compressed domain, which brings multi-fold advantages over the existing pipeline: 1) Compared to raw images from the decoded video, the compressed video, consisting of I-frames, motion vectors and residuals, is highly distinguishable, which allows us to leverage the entire video for learning without manual sampling through a specialized model design; 2) The captioning model is more efficient in inference as smaller and less redundant information is processed.
Ranked #8 on
Video Captioning
on VATEX
Local Compressed Video Stream Learning for Generic Event Boundary Detection
Specifically, we use lightweight ConvNets to extract features of the P-frames in the GOPs and spatial-channel attention module (SCAM) is designed to refine the feature representations of the P-frames based on the compressed information with bidirectional information flow.
Flow-Guided Diffusion for Video Inpainting
Video inpainting has been challenged by complex scenarios like large movements and low-light conditions.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
Ranked #2 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles.
SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute Recognition
The AFSS module learns to provide reasonable scale prior information for different attribute groups, allowing the model to focus on different levels of feature maps with varying semantic granularity.
Context-Guided Spatio-Temporal Video Grounding
The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context.
Ranked #1 on
Spatio-Temporal Video Grounding
on HC-STVG1
Neural Radiance Fields in Medical Imaging: Challenges and Next Steps
Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data.
VastTrack: Vast Category Visual Object Tracking
The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking.
Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
The shared embeddings, which describe the absolute coordinates of multi-resolution images (namely, the template and search images), are inherited from the pre-trained backbones.
Beyond MOT: Semantic Multi-Object Tracking
Current multi-object tracking (MOT) aims to predict trajectories of targets (i. e.,"where") in videos.
S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document
S3LLM demonstrates the potential of using locally deployed open-source LLMs for the rapid understanding of large-scale scientific computing software, eliminating the need for extensive coding expertise, and thereby making the process more efficient and effective.
Benchmarking the Robustness of UAV Tracking Against Common Corruptions
Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions.
