Search Results for author:
Found 127 papers, 64 papers with code
CompenHR: Efficient Full Compensation for High-resolution Projector
Full projector compensation is a practical task of projector-camera systems.
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment
It is important to quantify Damage Assessment (DA) for Human Assistance and Disaster Response (HADR) applications.
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models.
Salient Object Detection in Optical Remote Sensing Images Driven by Transformer
KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features.
Transparent Object Tracking with Enhanced Fusion Module
However, with the existing fusion techniques, the addition of new features causes a change in the latent space making it impossible to incorporate transparency awareness on trackers with fixed latent spaces.
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Compared with classical open cholecystectomy, laparoscopic cholecystectomy (LC) is associated with significantly shorter recovery period, and hence is the preferred method.
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.
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.
Free-Form Composition Networks for Egocentric Action Recognition
We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.
DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation
Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue.
CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network
Firstly, CheckerPose densely samples 3D keypoints from the surface of the 3D object and finds their 2D correspondences progressively in the 2D image.
Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder
It detects possible triggers in the token space using image structural similarity and label consistency between the test image and MAE restorations.
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators.
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.
The Cascaded Forward Algorithm for Neural Network Training
Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning.
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.
Backdoor Cleansing with Unlabeled Data
In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels.
Local Context-Aware Active Domain Adaptation
In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue.
Domain Adaptation with Adversarial Training on Penultimate Activations
We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works.
Expanding Language-Image Pretrained Models for General Video Recognition
Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios.
Ranked #7 on
Zero-Shot Action Recognition
on Kinetics
Prior Knowledge Guided Unsupervised Domain Adaptation
We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels.
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis.
Safe Self-Refinement for Transformer-based Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.
Unsupervised Deep Learning Meets Chan-Vese Model
The Chan-Vese (CV) model is a classic region-based method in image segmentation.
Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder.
EDTER: Edge Detection with Transformer
In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches.
Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography
With the structural information and appearance feature from noisy image as references, our model can remove larger BMA and produce better visualizing result.
Consistency and Diversity induced Human Motion Segmentation
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.
Deep Probabilistic Graph Matching
Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a relaxed assignment solver to obtain sub-optimal correspondences.
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild
The first stage is Forward Propagation, where the features from frame t-1 are propagated to frame t based on previously detected hands and their estimated motion.
Ranked #1 on
Multiple Object Tracking
on YouTube-Hands
(using extra training data)
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
Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD.
Searching the Search Space of Vision Transformer
Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures.
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.
Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications
Many critical applications rely on cameras to capture video footage for analytical purposes.
Joint Graph Learning and Matching for Semantic Feature Correspondence
In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.
Adaptive Edge Attention for Graph Matching with Outliers
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
Ranked #7 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning
The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model.
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages.
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.
AutoFormer: Searching Transformers for Visual Recognition
Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch.
Ranked #1 on
Fine-Grained Image Classification
on Oxford 102 Flowers
(Top 1 Accuracy metric)
CBNet: A Composite Backbone Network Architecture for Object Detection
With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
Ranked #6 on
Object Detection
on COCO-O
(using extra training data)
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discriminative features.
TransCamP: Graph Transformer for 6-DoF Camera Pose Estimation
Camera pose estimation or camera relocalization is the centerpiece in numerous computer vision tasks such as visual odometry, structure from motion (SfM) and SLAM.
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges.
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs.
Ranked #4 on
Multi-Object Tracking
on MOT16
(using extra training data)
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy.
On the Robustness of Multi-View Rotation Averaging
Rotation averaging is a synchronization process on single or multiple rotation groups, and is a fundamental problem in many computer vision tasks such as multi-view structure from motion (SfM).
Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction.
PoGO-Net: Pose Graph Optimization With Graph Neural Networks
Accurate camera pose estimation or global camera re-localization is a core component in Structure-from-Motion (SfM) and SLAM systems.
Hypergraph Neural Networks for Hypergraph Matching
Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph.
Ranked #9 on
Graph Matching
on Willow Object Class
Modeling Deep Learning Based Privacy Attacks on Physical Mail
Mail privacy protection aims to prevent unauthorized access to hidden content within an envelope since normal paper envelopes are not as safe as we think.
Political Posters Identification with Appearance-Text Fusion
In this paper, we propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters from other similar political images.
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers
Light-based adversarial attacks use spatial augmented reality (SAR) techniques to fool image classifiers by altering the physical light condition with a controllable light source, e. g., a projector.
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.
GMOT-40: A Benchmark for Generic Multiple Object Tracking
Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years.
Transparent Object Tracking Benchmark
To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking.
Pushing the Envelope of Rotation Averaging for Visual SLAM
As an essential part of structure from motion (SfM) and Simultaneous Localization and Mapping (SLAM) systems, motion averaging has been extensively studied in the past years and continues to attract surging research attention.
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.
Feature Space Augmentation for Long-Tailed Data
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different.
Ranked #24 on
Long-tail Learning
on Places-LT
End-to-end Full Projector Compensation
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.
Recurrent Exposure Generation for Low-Light Face Detection
REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
Cross-Modal Weighting Network for RGB-D Salient Object Detection
In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.
Ranked #9 on
RGB-D Salient Object Detection
on NJU2K
Deep Bilateral Retinex for Low-Light Image Enhancement
Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.
Cyclic Differentiable Architecture Search
First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized.
Ranked #16 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Graph Neural Network for Hamiltonian-Based Material Property Prediction
Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties.
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation.
TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge
Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed.
Cascaded Human-Object Interaction Recognition
The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction.
DeProCams: Simultaneous Relighting, Compensation and Shape Reconstruction for Projector-Camera Systems
In this paper, we propose a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks.
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.
Human-Aware Motion Deblurring
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG).
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.
MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection
Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection.
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.
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
LaFIn: Generative Landmark Guided Face Inpainting
It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.
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.
Improving Human Annotation in Single Object Tracking
We then use different techniques to smooth the trajectories at certain degree.
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #44 on
Instance Segmentation
on COCO test-dev
CompenNet++: End-to-end Full Projector Compensation
In this paper, we propose the first end-to-end solution, named CompenNet++, to solve the two problems jointly.
Hybrid Camera Pose Estimation with Online Partitioning for SLAM
This paper presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to monocular Simultaneous Localization and Mapping (SLAM) systems.
Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement
In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result.
Ranked #13 on
Face Alignment
on 300W
Graph Attribute Aggregation Network with Progressive Margin Folding
Graph convolutional neural networks (GCNNs) have been attracting increasing research attention due to its great potential in inference over graph structures.
Salient Object Detection in the Deep Learning Era: An In-Depth Survey
As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years.
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).
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking
Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters.
End-to-end Projector Photometric Compensation
Such benchmark is not previously available, to our best knowledge, due to the fact that conventional evaluation requests the hardware system to actually project the final results.
Generic Multiview Visual Tracking
A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets (e. g. human), static cameras, and/or camera calibration.
PFLD: A Practical Facial Landmark Detector
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).
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.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
C-RPN is trained end-to-end with the multi-task loss function.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #148 on
Object Detection
on COCO test-dev
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.
StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity.
Robotics
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.
Privacy-Protective-GAN for Face De-identification
In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.
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.
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
More specifically, MTFH exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable.
Vision Meets Drones: A Challenge
In this paper we present a large-scale visual object detection and tracking benchmark, named VisDrone2018, aiming at advancing visual understanding tasks on the drone platform.
Graph Correspondence Transfer for Person Re-identification
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets
Existing camera-projector calibration methods typically warp feature points from a camera image to a projector image using estimated homographies, and often suffer from errors in camera parameters and noise due to imperfect planarity of the calibration target.
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.
Saliency Pattern Detection by Ranking Structured Trees
We show that the linear combination of structured labels can well model the saliency distribution in local regions.
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.
Transductive Zero-Shot Learning with a Self-training dictionary approach
As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data.
Planar Object Tracking in the Wild: A Benchmark
In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment.
LIME: Low-light Image Enhancement via Illumination Map Estimation
When one captures images in low-light conditions, the images often suffer from low visibility.
Ranked #1 on
Low-Light Image Enhancement
on 10 Monkey Species
(using extra training data)
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.
Tensor Power Iteration for Multi-Graph Matching
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic.
A Richly Annotated Dataset for Pedestrian Attribute Recognition
RAP has in total 41, 585 pedestrian samples, each of which is annotated with 72 attributes as well as viewpoints, occlusions, body parts information.
A Comparative Study of Object Trackers for Infrared Flying Bird Tracking
Bird strikes present a huge risk for aircraft, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation.
3D Hand Pose Estimation Using Randomized Decision Forest With Segmentation Index Points
In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework.
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner.
Cross-Age Face Verification by Coordinating With Cross-Face Age Verification
As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification.
Adaptive Objectness for Object Tracking
Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.
Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation
Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion.
Saliency Detection on Light Field
Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions.
Multi-target Tracking with Motion Context in Tensor Power Iteration
In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.
3D R Transform on Spatio-temporal Interest Points for Action Recognition
In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points.
Multi-target Tracking by Rank-1 Tensor Approximation
In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an 1 norm tensor power iteration solution.
