Search Results for author:
Found 47 papers, 9 papers with code
Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer
Multiple complex degradations are coupled in low-quality video faces in the real world.
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
Trustworthy Large Models in Vision: A Survey
The rapid progress of Large Models (LMs) has recently revolutionized various fields of deep learning with remarkable grades, ranging from Natural Language Processing (NLP) to Computer Vision (CV).
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data
We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained.
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer
Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited data of target domains, which follows a domain-level knowledge transfer to learn a shared posterior distribution.
Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network
In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content.
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area of research and proven to be effective for various VL tasks such as visual-question answering.
Meta Compositional Referring Expression Segmentation
Then, following a novel meta optimization scheme to optimize the model to obtain good testing performance on the virtual testing sets after training on the virtual training set, our framework can effectively drive the model to better capture semantics and visual representations of individual concepts, and thus obtain robust generalization performance even when handling novel compositions.
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network
To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes.
SDRTV-to-HDRTV Conversion via Spatial-Temporal Feature Fusion
To evaluate the performance of the proposed method, we construct a corresponding multi-frame dataset using HDR video of the HDR10 standard to conduct a comprehensive evaluation of different methods.
Individualized Conditioning and Negative Distances for Speaker Separation
Speaker separation aims to extract multiple voices from a mixed signal.
Heatmap Distribution Matching for Human Pose Estimation
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping
Joint super-resolution and inverse tone-mapping (SR-ITM) aims to enhance the visual quality of videos that have quality deficiencies in resolution and dynamic range.
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video.
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).
Transcoded Video Restoration by Temporal Spatial Auxiliary Network
In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers.
Statistical Perspectives on Reliability of Artificial Intelligence Systems
We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification, and discuss how those topics can be related to AI reliability, with illustrative examples.
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order.
The Multi-Modal Video Reasoning and Analyzing Competition
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021.
Efficient Two-Step Networks for Temporal Action Segmentation
Due to boundary ambiguity and over-segmentation issues, identifying all the frames in long untrimmed videos is still challenging.
Ranked #12 on
Action Segmentation
on GTEA
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10, 080 in-the-wild videos and annotated 62, 535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios.
Ranked #2 on
Video Question Answering
on SUTD-TrafficQA
Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach
We develop a landscape-flux framework to investigate observed frequency distributions of vegetation and the stability of these ecological systems under fluctuations.
Enhancement of Superconductivity Linked with Linear-in-Temperature/Field Resistivity in Ion-Gated FeSe Films
Iron selenide (FeSe) - the structurally simplest iron-based superconductor, has attracted tremendous interest in the past years.
Superconductivity
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems.
Modelling Universal Order Book Dynamics in Bitcoin Market
Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists.
Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data
In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier.
Effective Domain Knowledge Transfer with Soft Fine-tuning
However, the datasets from source domain are simply discarded in the fine-tuning process.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms.
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable.
Shepard Convolutional Neural Networks
In this paper, we draw on Shepard interpolation and design Shepard Convolutional Neural Networks (ShCNN) which efficiently realizes end-to-end trainable TVI operators in the network.
Mutual-Structure for Joint Filtering
Previous joint/guided filters directly transfer the structural information in the reference image to the target one.
Deep Multimodal Speaker Naming
Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video.
Just Noticeable Defocus Blur Detection and Estimation
We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images.
Handling Motion Blur in Multi-Frame Super-Resolution
Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR).
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks
We recently have witnessed many ground-breaking results in machine learning and computer vision, generated by using deep convolutional neural networks (CNN).
Deep Convolutional Neural Network for Image Deconvolution
Many fundamental image-related problems involve deconvolution operators.
Ranked #1 on
Image Compression
on FER2013
Hierarchical Saliency Detection on Extended CSSD
Complex structures commonly exist in natural images.
An Evasion and Counter-Evasion Study in Malicious Websites Detection
Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism.
Discriminative Blur Detection Features
Ubiquitous image blur brings out a practically important question what are effective features to differentiate between blurred and unblurred image regions.
Joint Depth Estimation and Camera Shake Removal from Single Blurry Image
The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene.
100+ Times Faster Weighted Median Filter (WMF)
Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation.
2D Semantic Segmentation task 1 (8 classes)
Optical Flow Estimation
+2
Dense Scattering Layer Removal
We propose a new model, together with advanced optimization, to separate a thick scattering media layer from a single natural image.
Unnatural L0 Sparse Representation for Natural Image Deblurring
We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures.
Ranked #13 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Hierarchical Saliency Detection
When dealing with objects with complex structures, saliency detection confronts a critical problem namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns.
