Search Results for author:
Found 67 papers, 26 papers with code
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
Salient object detection (SOD) aims to determine the most visually attractive objects in an image.
Feedback Chain Network For Hippocampus Segmentation
The feedback chain structure unit learns deeper and wider feature representation of each encoder layer through the hierarchical feature aggregation feedback chains, and achieves feature selection and feedback through the feature handover attention module.
PSNet: Parallel Symmetric Network for Video Salient Object Detection
Finally, we use the Importance Perception Fusion (IPF) module to fuse the features from two parallel branches according to their different importance in different scenarios.
HVS Revisited: A Comprehensive Video Quality Assessment Framework
Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed.
Ranked #4 on
Video Quality Assessment
on LIVE-FB LSVQ
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase.
Just Noticeable Difference Modeling for Face Recognition System
In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
The underlying mechanism of the proposed approach is based upon the wild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion.
Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Ranked #8 on
RGB Salient Object Detection
on PASCAL-S
High Dynamic Range Image Quality Assessment Based on Frequency Disparity
In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM).
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images.
BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload.
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images.
Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection
Considering the inaccurate depth map issue, we collect the RGB features of early stages into a skip convolution module to give more guidance from RGB modality to the final saliency prediction.
Multi-View Video Coding with GAN Latent Learning
The introduction of multiple viewpoints inevitably increases the bitrates to store and transmit video scenes.
Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation
The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding
To further improve the performance of intra coding in Versatile Video Coding (VVC), an intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD).
Two-stream Hierarchical Similarity Reasoning for Image-text Matching
Second, previous approaches only consider learning single-stream similarity alignment (i. e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching.
Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection
In this paper, we propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$^o$ videos projected via equirectangular projection (ERP) format.
Generalized Visual Quality Assessment of GAN-Generated Face Images
However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model.
Crowd Counting in the Frequency Domain
By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is simple, effective, and efficient.
CSformer: Bridging Convolution and Transformer for Compressive Sensing
The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery.
Learning Transformer Features for Image Quality Assessment
In addition, comparable NR performance is achieved in extensive experiments, and the results show that the NR performance can be leveraged by the joint training scheme.
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch.
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
The other is the content guidance bridge (CGBdg) designed for the depth map reconstruction process, which provides the content guidance learned from DSR task for MDE task.
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society.
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder
Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric for image captioning, which we call Intrinsic Image Captioning Evaluation($I^2CE$).
Self-supervised Symmetric Nonnegative Matrix Factorization
Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information.
On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)
Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain.
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains.
Intrinsic Image Captioning Evaluation
The image captioning task is about to generate suitable descriptions from images.
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
Consequently, we devise a spatial regularization and propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel and generate the content-aware superpixels with more detailed boundaries.
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.
Deep Magnification-Flexible Upsampling over 3D Point Clouds
In addition, we propose a simple yet effective training strategy to drive such a flexible ability.
Controllable Pareto Multi-Task Learning
With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among learning all of them together.
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network
We proposed a new I2I translation method that generates a new model in the target domain via a series of model transformations on a pre-trained StyleGAN2 model in the source domain.
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution
To preserve the parallax structure among the reconstructed SAIs, we subsequently append a consistency regularization network trained over a structure-aware loss function to refine the parallax relationships over the coarse estimation.
Towards Modality Transferable Visual Information Representation with Optimal Model Compression
Compactly representing the visual signals is of fundamental importance in various image/video-centered applications.
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display
The impact of distorted geometry and texture attributes is further discussed in this paper.
Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems
Samples in the majority class are divided into many subspaces by a hashing method.
Multi-Density Sketch-to-Image Translation Network
Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization.
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applications applied to the downsampled sparse points as much as possible.
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
On the basis of the novel tensor low-rank norm, we formulate MVSC as a convex low-rank tensor recovery problem, which is then efficiently solved with an augmented Lagrange multiplier based method iteratively.
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension.
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network
In addition, our method preserves the light field parallax structure better.
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Ranked #1 on
Low-Light Image Enhancement
on NPE
(User Study Score metric)
Pareto Multi-Task Learning
Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video Streaming over HTTP
The proposed algorithm is proxy-free.
An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP
Specifically, the proposed framework is composed of two modules, i. e., the method pool and method controller.
Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach
Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication.
Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos
That is, our method preserves both the quality and the smoothness of tiles in FoV, thus providing the best QoE for users.
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment
First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images.
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion
Specifically, the coarse sub-aperture image (SAI) synthesis module first explores the scene geometry from an unstructured sparsely-sampled LF and leverages it to independently synthesize novel SAIs, in which a confidence-based blending strategy is proposed to fuse the information from different input SAIs, giving an intermediate densely-sampled LF.
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order.
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses
To the best of our knowledge, this is the first end-to-end deep learning method for reconstructing a high-resolution LF image with a hybrid input.
Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI.
Clustering-aware Graph Construction: A Joint Learning Perspective
Graph-based clustering methods have demonstrated the effectiveness in various applications.
An Underwater Image Enhancement Benchmark Dataset and Beyond
In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images.
Ranked #5 on
Underwater Image Restoration
on LSUI
(using extra training data)
Nonlinear Collaborative Scheme for Deep Neural Networks
Different from them, in this paper, we aim to link the generalization ability of a deep network to optimizing a new objective function.
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
The proposed algorithm uses the Bayesian neural network as the scalable surrogate model.
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
It decomposes a multi-objective optimization problem into several single-objective optimization subproblems, each of which is usually defined as a scalarizing function using a weight vector.
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
In particular, the stable matching between subproblems and solutions, which achieves an equilibrium between their mutual preferences, implicitly strikes a balance between the convergence and diversity.
