Search Results for author:
Found 84 papers, 22 papers with code
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
Deep Frequency Filtering for Domain Generalization
Improving the generalization capability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.
ActiveMLP: An MLP-like Architecture with Active Token Mixer
This paper presents ActiveMLP, a general MLP-like backbone for computer vision.
Ranked #33 on
Object Detection
on COCO minival
Mask-based Latent Reconstruction for Reinforcement Learning
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance.
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks
Traditional media coding schemes typically encode image/video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level.
Learning Cross-Scale Prediction for Efficient Neural Video Compression
In this paper, we present the first neural video codec that can compete with the latest coding standard H. 266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode.
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks.
Confounder Identification-free Causal Visual Feature Learning
In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders.
Few-Shot Real Image Super-resolution via Distortion-Relation Guided Transfer Learning
DRTL assigns a knowledge graph to capture the distortion relation between auxiliary tasks (i. e., synthetic distortions) and target tasks (i. e., real distortions with few images), and then adopt a gradient weighting strategy to guide the knowledge transfer from auxiliary task to target task.
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.
Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels
Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data.
Versatile Learned Video Compression
We show that the flow prediction module can largely reduce the transmission cost of voxel flows.
Unleash the Potential of Adaptation Models via Dynamic Domain Labels
In this paper, we propose an embarrassing simple yet highly effective adversarial domain adaptation (ADA) method for effectively training models for alignment.
ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain.
Partial Person Re-Identification With Part-Part Correspondence Learning
Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID).
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
In this work, we propose a novel method, dubbed PlayVirtual, which augments cycle-consistent virtual trajectories to enhance the data efficiency for RL feature representation learning.
Continuous Control (100k environment steps)
Continuous Control (500k environment steps)
+2
Task-driven Semantic Coding via Reinforcement Learning
However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process.
Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment
In the hypergraph construction, we build a location-based hyperedge and a content-based hyperedge for each viewport.
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness
In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness.
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Quantization is one of the core components in lossy image compression.
Bayesian Graph Convolutional Network for Traffic Prediction
Under this framework, the graph structure is viewed as a random realization from a parametric generative model, and its posterior is inferred using the observed topology of the road network and traffic data.
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation
For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics.
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation.
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions.
Local Patch AutoAugment with Multi-Agent Collaboration
We formulate it as a multi-agent reinforcement learning (MARL) problem, where each agent learns an augmentation policy for each patch based on its content together with the semantics of the whole image.
Dense Interaction Learning for Video-based Person Re-identification
The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames.
Ranked #1 on
Person Re-Identification
on DukeMTMC-reID
No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness.
Image-to-Image Translation: Methods and Applications
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations.
Style Normalization and Restitution for Domain Generalization and Adaptation
In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks.
Learned Block-based Hybrid Image Compression
Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications.
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i. e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations.
Deep Multi-Scale Features Learning for Distorted Image Quality Assessment
In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction.
Causal Contextual Prediction for Learned Image Compression
In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space.
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
In addition, by transferring knowledge from other kinds of MRC tasks, our model achieves a new state-of-the-art results in both single and ensemble settings.
Ranked #1 on
Reading Comprehension
on RACE
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.
Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting
The graph structure in our network is learned from the physical topology of the road network and traffic data in an end-to-end manner, which discovers a more accurate description of the relationship among traffic flows.
Uncertainty-Aware Few-Shot Image Classification
In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization.
FAN: Frequency Aggregation Network for Real Image Super-resolution
Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions.
Feature Alignment and Restoration for Domain Generalization and Adaptation
To ensure high discrimination, we propose a Feature Restoration (FR) operation to distill task-relevant features from the residual information and use them to compensate for the aligned features.
Ranked #49 on
Domain Generalization
on PACS
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification
There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID.
Residual Squeeze-and-Excitation Network for Fast Image Deraining
Image deraining is an important image processing task as rain streaks not only severely degrade the visual quality of images but also significantly affect the performance of high-level vision tasks.
Global Distance-distributions Separation for Unsupervised Person Re-identification
To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.
Style Normalization and Restitution for Generalizable Person Re-identification
Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps.
Ranked #8 on
Unsupervised Domain Adaptation
on Market to Duke
Multi-scale Grouped Dense Network for VVC Intra Coding
Versatile Video Coding (H. 266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc.
Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions.
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details.
Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification
In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.
Prior-enlightened and Motion-robust Video Deblurring
Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed.
SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration
Accelerating the inference speed of CNNs is critical to their deployment in real-world applications.
VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge
This paper introduces VESR-Net, a method for video enhancement and super-resolution (VESR).
Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification
To the best of our knowledge, we are the first to make use of multi-shots of an object in a teacher-student learning manner for effectively boosting the single image based re-id.
Generative Memorize-Then-Recall framework for low bit-rate Surveillance Video Compression
In this paper, we figure out this issue by disentangling surveillance video into the structure of a global spatio-temporal feature (memory) for Group of Picture (GoP) and skeleton for each frame (clue).
Region Normalization for Image Inpainting
Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e. g. mean and variance shifts.
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics
Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc.
Tensor Oriented No-Reference Light Field Image Quality Assessment
Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media.
Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement
Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents.
Multimedia Image and Video Processing
No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement
In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency.
Progressive Image Inpainting with Full-Resolution Residual Network
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes.
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
In this paper, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images.
A Coarse-to-Fine Framework for Learned Color Enhancement with Non-Local Attention
For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously.
Learning to Transfer: Unsupervised Meta Domain Translation
Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data.
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
Image-to-image translation models have shown remarkable ability on transferring images among different domains.
Semantics-Aligned Representation Learning for Person Re-identification
Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image.
Image-to-Image Translation with Multi-Path Consistency Regularization
In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation $\mathcal{D}_s\to\mathcal{D}_t$ and indirect translation $\mathcal{D}_s\to\mathcal{D}_a\to\mathcal{D}_t$ with $\mathcal{D}_a$ as an auxiliary domain, to regularize training.
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing
We achieve this by the context interaction among the features of different scales.
Towards Accurate One-Stage Object Detection with AP-Loss
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
Relation-Aware Global Attention for Person Re-identification
For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matches well the key of re-id, i. e., discriminative feature learning.
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments
Not limited to the control tasks in computationally complex environments, AE-DDPG also achieves higher rewards and 2- to 4-fold improvement in sample efficiency on average compared to other variants of DDPG in MuJoCo environments.
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation
After pre-training, this network is used to extract the domain-specific features of each image.
View Invariant 3D Human Pose Estimation
The diversity of capturing viewpoints and the flexibility of the human poses, however, remain some significant challenges.
Learning based Facial Image Compression with Semantic Fidelity Metric
The experimental results verify the framework's efficiency by demonstrating performance improvement of 71. 41%, 48. 28% and 52. 67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric.
Densely Semantically Aligned Person Re-Identification
We propose a densely semantically aligned person re-identification framework.
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
The SGU sequentially takes information from two different levels as inputs and decides the output based on one active input.
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures.
Unsupervised Single Image Deraining with Self-supervised Constraints
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications.
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting.
Distribution Discrepancy Maximization for Image Privacy Preserving
Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed image from the encrypted image, and an encryption model $G_e$ is required to generate these two kinds of images to maximize the recognition rate of $D$, leading to the same training objective for both $D$ and $G_e$.
Multi-Scale Face Restoration with Sequential Gating Ensemble Network
Experiment results demonstrate that our SGEN is more effective at multi-scale human face restoration with more image details and less noise than state-of-the-art image restoration models.
Conditional Image-to-Image Translation
In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain.
Learning for Video Compression
One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network.
Multimedia Image and Video Processing
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
