Search Results for author:
Found 65 papers, 22 papers with code
Manifold Projection for Adversarial Defense on Face Recognition
We utilize variational autoencoder (VAE) to estimate the lower bound of the log-likelihood of image and explore to project the input images back into the high probability regions of image manifold again.
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion
This paper proposes ``\emph{${\bf M}$-magnitude weighted $L_{\bf 2}$ distortion + $\bf 2$ degrees of freedom''} (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs).
Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure
Stacking GLE modules enables the network to extract image features from different image frequency components.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases
Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases.
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
After finetuning the pretrained LoMaR on 384$\times$384 images, it can reach 85. 4% top-1 accuracy, surpassing MAE by 0. 6%.
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement
Previously proposed FullSubNet has achieved outstanding performance in Deep Noise Suppression (DNS) Challenge and attracted much attention.
A Unified Framework for Campaign Performance Forecasting in Online Display Advertising
Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campaign performance to optimize delivery strategies in advance, resulting in a waste of labour and budget for feedback adjustments.
An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification
We perform a deep dive into understanding the impact of (i) the input representation/type and (ii) the architectural layer of the neural network.
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval
We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc.
Towards Multi-Domain Single Image Dehazing via Test-Time Training
Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest.
Dynamically Stable Poincaré Embeddings for Neural Manifolds
As a result, we prove that, if initial metrics have an $L^2$-norm perturbation which deviates from the Hyperbolic metric on the Poincar\'e ball, the scaled Ricci-DeTurck flow of such metrics smoothly and exponentially converges to the Hyperbolic metric.
Video Frame Interpolation Transformer
Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.
Thoughts on the Consistency between Ricci Flow and Neural Network Behavior
During the training process of the neural network, we observe that its metric will also regularly converge to the linearly nearly Euclidean metric, which is consistent with the convergent behavior of linearly nearly Euclidean metrics under the Ricci-DeTurck flow.
whu-nercms at trecvid2021:instance search task
We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper.
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network
Despite recent improvement of supervised monocular depth estimation, the lack of high quality pixel-wise ground truth annotations has become a major hurdle for further progress.
Manifold Micro-Surgery with Linearly Nearly Euclidean Metrics
The Ricci flow is a method of manifold surgery, which can trim manifolds to more regular.
Riemannian Manifold Embeddings for Straight-Through Estimator
Quantized Neural Networks (QNNs) aim at replacing full-precision weights $\boldsymbol{W}$ with quantized weights $\boldsymbol{\hat{W}}$, which make it possible to deploy large models to mobile and miniaturized devices easily.
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck
We study the problem of cross-domain lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.
Universal Rate-Distortion-Perception Representations for Lossy Compression
In the context of lossy compression, Blau & Michaeli (2019) adopt a mathematical notion of perceptual quality and define the information rate-distortion-perception function, generalizing the classical rate-distortion tradeoff.
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets
In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition
This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing
By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps.
A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning
Recently, there has been rapid and significant progress on image dehazing.
Bayesian Optimisation for a Biologically Inspired Population Neural Network
The 8-dimensional optimal hyper-parameter combination should be such that the network dynamics simulate the resting state alpha rhythm (8 - 13 Hz rhythms in brain signals).
Towards a Unified Approach to Single Image Deraining and Dehazing
We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit.
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing
We also propose a novel intra-task knowledge transfer mechanism that can memorize and take advantage of synthetic domain knowledge to assist the learning process on the translated data.
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations.
Learning for Unconstrained Space-Time Video Super-Resolution
Recent years have seen considerable research activities devoted to video enhancement that simultaneously increases temporal frame rate and spatial resolution.
Optical Flow Estimation
Space-time Video Super-resolution
+2
VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning
To the best of our knowledge, this is the first work that improves data efficiency of image captioning by utilizing LM pretrained on unimodal data.
The configuration and failed eruption of a complex magnetic flux rope above a δ sunspot region
A null point is located above the flux rope.
Solar and Stellar Astrophysics Space Physics
Indirect Domain Shift for Single Image Dehazing
Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images.
Edge-Featured Graph Attention Network
In this paper, we present edge-featured graph attention networks, namely EGATs, to extend the use of graph neural networks to those tasks learning on graphs with both node and edge features.
Optimizing Quantized Neural Networks with Natural Gradient
Quantized Neural Networks (QNNs) have achieved an enormous step in improving computational efficiency, making it possible to deploy large models to mobile and miniaturized devices.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data.
Video Frame Interpolation via Generalized Deformable Convolution
Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies.
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference
Extensive experiments demonstrate that owing to the informativeness of the camera raw data, the effectiveness of the network architecture, and the separation of super-resolution and color correction processes, the proposed method achieves superior VSR results compared to the state-of-the-art and can be adapted to any specific camera-ISP.
AWNet: Attentive Wavelet Network for Image ISP
In this paper, we introduce a novel network that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to tackle this learnable image ISP problem.
Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs
The automatic text-based diagnosis remains a challenging task for clinical use because it requires appropriate balance between accuracy and interpretability.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Efficient long-distance relation extraction with DG-SpanBERT
In natural language processing, relation extraction seeks to rationally understand unstructured text.
Ranked #15 on
Relation Extraction
on TACRED
A Learning Framework for n-bit Quantized Neural Networks toward FPGAs
Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs.
Exploring Long Tail Visual Relationship Recognition with Large Vocabulary
We use these benchmarks to study the performance of several state-of-the-art long-tail models on the LTVRR setup.
Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks
Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation
Video super-resolution aims at generating a high-resolution video from its low-resolution counterpart.
Highly fluorescent copper nanoclusters for sensing and bioimaging
Metal nanoclusters (NCs), typically consisting of a few to tens of metal atoms, bridge the gap between organometallic compounds and crystalline metal nanoparticles.
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned.
Ranked #4 on
Image Dehazing
on RS-Haze
Discriminative Consistent Domain Generation for Semi-supervised Learning
Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.
A Fast Free-viewpoint Video Synthesis Algorithm for Sports Scenes
In this paper, we report on a parallel freeviewpoint video synthesis algorithm that can efficiently reconstruct a high-quality 3D scene representation of sports scenes.
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis.
Ranked #8 on
Image Dehazing
on SOTS Outdoor
Keyphrase Generation with Correlation Constraints
In this paper, we study automatic keyphrase generation.
Action Recognition for Depth Video using Multi-view Dynamic Images
To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed.
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
Efficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy
This paper proposes a new parallel approach to solve connected components on a 2D binary image implemented with CUDA.
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map.
An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs
In this paper, we report an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing the GPU architecture.
A straightforward method to assess motion blur for different types of displays
A simulation method based on the liquid crystal response and the human visual system is suitable to characterize motion blur for LCDs but not other display types.
