Search Results for author:
Found 83 papers, 15 papers with code
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy.
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization
Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling.
PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image
As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc.
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features.
New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning
In all settings, the online class incremental learning (CIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
Take a Prior from Other Tasks for Severe Blur Removal
For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e. g. classification) to learn a comprehensive prior in severe blur removal cases.
Multispectral Pedestrian Detection via Reference Box Constrained Cross Attention and Modality Balanced Optimization
Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information especially under low light conditions.
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance
To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data.
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
The key insight of this design is that the cross-attention mechanism in the transformer could be an ideal solution to align the discriminative texture clues from the original image with the canonical view image, which could compensate for the low-quality texture information of the canonical view image.
Domain Generalization
Generalizable Person Re-identification
+1
A Simple and Robust Correlation Filtering Method for Text-based Person Search
Text-based person search aims to associate pedestrian images with natural language descriptions.
Ranked #2 on
Text based Person Retrieval
on CUHK-PEDES
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
Multi-stage image denoising with the wavelet transform
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection
For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame.
Ranked #1 on
Anomaly Detection
on Corridor
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information.
PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening
Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network.
Learning Correspondency in Frequency Domain by a Latent-Space Similarity Loss for Multispectral Pansharpening
The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening.
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications.
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model
Computer vision models for image quality assessment (IQA) predict the subjective effect of generic image degradation, such as artefacts, blurs, bad exposure, or colors.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP).
An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection
DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved.
Audio-visual speech separation based on joint feature representation with cross-modal attention
Multi-modal based speech separation has exhibited a specific advantage on isolating the target character in multi-talker noisy environments.
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos
For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information.
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign
The former works only for a relatively small perturbation 8/255 with the l_\infty constraint, and GradAlign improves it by extending the perturbation size to 16/255 (with the l_\infty constraint) but at the cost of being 3 to 4 times slower.
Multi-Domain Joint Training for Person Re-Identification
Our discovery is that training with such an adaptive model can better benefit from more training samples.
Learnable Locality-Sensitive Hashing for Video Anomaly Detection
In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly.
NAS-FCOS: Efficient Search for Object Detection Architectures
Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures.
Text-based Person Search in Full Images via Semantic-Driven Proposal Generation
Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance. However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images.
Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking
To solve this problem, we propose to distill representations of the TIR modality from the RGB modality with Cross-Modal Distillation (CMD) on a large amount of unlabeled paired RGB-TIR data.
Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network
Specifically, the generator employs a fully convolutional sequence network to extract global representation of a video, and an attention-based network to output normalized importance scores.
Center Prediction Loss for Re-identification
In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.
Dynamic Image Restoration and Fusion Based on Dynamic Degradation
In addition, a dynamic degradation kernel is proposed to improve the robustness of image restoration and fusion.
Efficient Spatialtemporal Context Modeling for Action Recognition
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification
Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under different viewpoints.
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance
While the mappings between image and pixel-wise depth are well-studied in current methods, the correlation between image, depth and scene semantics, however, is less considered.
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Based on this framework, we enhance the local feature representation by sampling and feeding the point-based features that locate on the semantic edges to an individual Semantic-guided Edge Enhancement module (SEEM), which is specifically designed for promoting depth estimation on the challenging semantic borders.
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution
One of the main reason comes from the fact that the predefined degeneration models (e. g. blur in spatial domain) utilized by most HSI SR methods often exist great discrepancy with the real one, which results in these deep models overfit and ultimately degrade their performance on real data.
Meta-Generating Deep Attentive Metric for Few-shot Classification
To mitigate this problem, we present a novel deep metric meta-generation method that turns to an orthogonal direction, ie, learning to adaptively generate a specific metric for a new FSL task based on the task description (eg, a few labelled samples).
On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration.
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Fashion products typically feature in compositions of a variety of styles at different clothing parts.
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application.
AE-Netv2: Optimization of Image Fusion Efficiency and Network Architecture
Finally, we explore the commonness and characteristics of different image fusion tasks, which provides a research basis for further research on the continuous learning characteristics of human brain in the field of image fusion.
3D Correspondence Grouping with Compatibility Features
We present a simple yet effective method for 3D correspondence grouping.
AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism
Firstly, the relationship between human brain cognitive mechanism and image fusion task is analyzed and a physical model is established to simulate human brain cognitive mechanism.
IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality
This paper presents an illumination estimation method for virtual objects in real environment by learning.
A Robust Attentional Framework for License Plate Recognition in the Wild
On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly.
Attention-based network for low-light image enhancement
Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning
First, we introduce a semantic alignment loss to align the relation statistics of the features from samples that belong to the same category.
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs.
Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention
In order to improve the robustness and contextual awareness of image fusion tasks, we proposed a multi-task auxiliary learning image fusion theory guided by subjective attention.
Non-linear and Selective Fusion of Cross-Modal Images
The human visual perception system has strong robustness in image fusion.
A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics
The characteristics of feature selection, nonlinear combination and multi-task auxiliary learning mechanism of the human visual perception system play an important role in real-world scenarios, but the research of image fusion theory based on the characteristics of human visual perception is less.
Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation
The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance.
Cross-Modality Person Re-identification
Person Re-Identification
Person Re-identification in Aerial Imagery
Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently.
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching
Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision.
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching
The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description.
NAS-FCOS: Fast Neural Architecture Search for Object Detection
The success of deep neural networks relies on significant architecture engineering.
Ranked #124 on
Object Detection
on COCO test-dev
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.
A Holistic Representation Guided Attention Network for Scene Text Recognition
In this work, we propose a simple yet strong approach for scene text recognition.
Vehicle Re-identification in Aerial Imagery: Dataset and Approach
It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks.
MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Compared to existing methods, MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization.
A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency
Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care.
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain.
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.
Learning Deep Gradient Descent Optimization for Image Deconvolution
Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering
However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters.
Self-Paced Kernel Estimation for Robust Blind Image Deblurring
Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the estimation process.
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
The prior for the non-low-rank structure is established based on a mixture of Gaussians which is shown to be flexible enough, and powerful enough, to inform the completion process for a variety of real tensor data.
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.
Pairwise Matching Through Max-Weight Bipartite Belief Propagation
Feature matching is a key problem in computer vision and pattern recognition.
Blind Image Deconvolution by Automatic Gradient Activation
We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not.
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.
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior
To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity
To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution
Based on the fact that singular primitive patches are more invariant to the scale change (i. e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation.
Single Image Super-resolution using Deformable Patches
We proposed a deformable patches based method for single image super-resolution.
Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations
We show that using Marginal Polytope Diagrams allows the number of constraints to be reduced without loosening the LP relaxations.
Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior
This paper presents a robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations.
Part-Based Visual Tracking with Online Latent Structural Learning
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking.
