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Found 368 papers, 140 papers with code
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion.
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text
Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators.
End-to-end Speech Translation System Description of LIT for IWSLT 2019
We propose layer-tied self-attention for end-to-end speech translation.
CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization
In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways.
LexiClean: An annotation tool for rapid multi-task lexical normalisation
LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide.
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention
On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features.
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal human effort.
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process
Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models.
Q-Cogni: An Integrated Causal Reinforcement Learning Framework
We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning with an autonomous causal structure discovery method to improve the learning process with causal inference.
ChatAug: Leveraging ChatGPT for Text Data Augmentation
A natural and widely-used strategy to mitigate such challenges is to perform data augmentation on the training data to better capture the data invariance and increase the sample size.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring
Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i. e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation.
An ASR-free Fluency Scoring Approach with Self-Supervised Learning
A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for sample reuse.
Design Booster: A Text-Guided Diffusion Model for Image Translation with Spatial Layout Preservation
Specifically, our method co-encodes images and text into a new domain during the training phase.
CFFT-GAN: Cross-domain Feature Fusion Transformer for Exemplar-based Image Translation
In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions.
HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
Unfortunately, this causes 3D pose estimation to fail in difficult cases such as $\textit{joints overlapping}$, and pose $\textit{fast-changing}$, as pair-wise relations cannot exploit fine-grained human body priors in pose estimation.
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing
Specifically, in Phase I, a W-space-oriented StyleGAN inversion network is trained and used to perform image inversion and editing, which assures the editability but sacrifices reconstruction quality.
Planning and Tracking Control of Full Drive-by-Wire Electric Vehicles in Unstructured Scenario
Full drive-by-wire electric vehicles (FDWEV) with X-by-wire technology can achieve independent driving, braking, and steering of each wheel, providing a good application platform for autonomous driving technology.
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Constructing Moral Discussions
In this paper, we propose a framework, MoralDial to train and evaluate moral dialogue systems.
Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation
Therefore, TAVS is distinguished from previous temporal segmentation datasets due to its multi-modal information, holistic view of categories, and hierarchical granularities.
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities.
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples
In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes.
Curriculum-based Asymmetric Multi-task Reinforcement Learning
To mitigate the negative influence of customizing the one-off training order in curriculum-based AMTL, CAMRL switches its training mode between parallel single-task RL and asymmetric multi-task RL (MTRL), according to an indicator regarding the training time, the overall performance, and the performance gap among tasks.
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
Second, we discuss the challenges of face restoration.
Model Compression for DNN-Based Text-Independent Speaker Verification Using Weight Quantization
The experiments on VoxCeleb indicate that quantization is effective for compressing SV models, where the model size can be reduced by multiple times with no noticeable performance decline.
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications
Transportation is the backbone of the economy and urban development.
Online LiDAR-Camera Extrinsic Parameters Self-checking
This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks.
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
Typically, conventional variational inference models are parameterized by neural networks independent of the latent dynamics models.
FR: Folded Rationalization with a Unified Encoder
Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected pieces.
Towards In-distribution Compatibility in Out-of-distribution Detection
Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions.
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task
However, CL on VQA involves not only the expansion of label sets (new Answer sets).
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS).
Ranked #4 on
Semi-Supervised Video Object Segmentation
on MOSE
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection
We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path.
CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)
The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD).
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation
To this end, we propose a novel NoIsy label CorrEction and Sample Training strategy for SGG: NICEST.
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips
The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications.
Towards Efficient Adversarial Training on Vision Transformers
Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention.
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems.
Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization
2) Secondly, a text-guided information range minimization method is proposed to adaptively encode descriptive parts of each modality into an identical space with a powerful pretrained linguistic model.
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge.
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades.
Towards Generalizable Person Re-identification with a Bi-stream Generative Model
Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors.
Domain Generalization
Generalizable Person Re-identification
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification
This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels.
Unsupervised Knowledge Adaptation for Passenger Demand Forecasting
These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share data among them.
Egocentric Video-Language Pretraining
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
Ranked #2 on
Action Recognition
on Charades-Ego
Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis
Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models.
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech
The performance of child speech recognition is generally less satisfactory compared to adult speech due to limited amount of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks
With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before.
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity
Moreover, we show the relationship between the new model and the original problem.
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs
Recent research found it beneficial to use large state spaces for HMMs and PCFGs.
Ranked #1 on
Constituency Grammar Induction
on PTB
Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning
They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text.
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition
A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes.
ChildPredictor: A Child Face Prediction Framework with Disentangled Learning
On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors.
Age-Invariant Face Recognition
Image-to-Image Translation
+1
XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation
The cross-modality encoder is pre-trained in a self-supervised manner to allow effective capture of cross- and intra-modality correlations, which facilitates the content-style disentanglement and modeling style representations of all scales (stroke-level, component-level and character-level).
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations
With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i. e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities.
Ranked #1 on
Video Retrieval
on MSVD
(using extra training data)
Improving Vision Transformers by Revisiting High-frequency Components
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on
Domain Generalization
on Stylized-ImageNet
Masked Autoencoders for Point Cloud Self-supervised Learning
Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches.
Ranked #2 on
Point Cloud Segmentation
on PointCloud-C
3D Part Segmentation
Few-Shot 3D Point Cloud Classification
+2
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
Sensor-based environmental perception is a crucial part of the autonomous driving system.
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
Once trained, the transformer can generate coherent image tokens based on the text embedding extracted from the text encoder of CLIP upon an input text.
Holistic Attention-Fusion Adversarial Network for Single Image Defogging
To address these issues, we develop a novel generative adversarial network, called holistic attention-fusion adversarial network (HAAN), for single image defogging.
An Unsupervised Attentive-Adversarial Learning Framework for Single Image Deraining
Moreover, to improve the transformation ability of C2R, we design a rain-fog feature decoupling and reorganization network (RFDR) by embedding a rainy image degradation model and a mixed discriminator to preserve richer texture details.
Exploring Structural Sparsity in Neural Image Compression
Neural image compression have reached or out-performed traditional methods (such as JPEG, BPG, WebP).
Constrained Variational Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications.
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection
Then we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group.
RFNet: Unsupervised Network for Mutually Reinforcing Multi-Modal Image Registration and Fusion
Specifically, for image registration, we solve the bottlenecks of defining registration metrics applicable for multi-modal images and facilitating the network convergence.
Coherent Point Drift Revisited for Non-Rigid Shape Matching and Registration
In this paper, we explore a new type of extrinsic method to directly align two geometric shapes with point-to-point correspondences in ambient space by recovering a deformation, which allows more continuous and smooth maps to be obtained.
DGL-GAN: Discriminator Guided Learning for GAN Compression
Generative Adversarial Networks (GANs) with high computation costs, e. g., BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high resolution and diverse images with high fidelity from random noises.
Triangle Attack: A Query-efficient Decision-based Adversarial Attack
Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label.
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization
We propose CO2Sum (Contrastive for Consistency), a contrastive learning scheme that can be easily applied on sequence-to-sequence models for factual-consistent abstractive summarization, proving that the model can be fact-aware without modifying the architecture.
MC-Blur: A Comprehensive Benchmark for Image Deblurring
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios.
Neural Routing by Memory
However, they use the same procedure sequence for all inputs, regardless of the intermediate features. This paper proffers a simple yet effective idea of constructing parallel procedures and assigning similar intermediate features to the same specialized procedures in a divide-and-conquer fashion.
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement
To alleviate the impact of few samples, enhancing the generalization and discrimination abilities of detectors on new objects plays an important role.
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction
In this paper, we aim to address the issue between datasets and models and propose a large scale stereo dataset with high accuracy disparity ground truth named PlantStereo.
Social Fraud Detection Review: Methods, Challenges and Analysis
Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection.
SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Spinal degeneration plagues many elders, office workers, and even the younger generations.
Meter-Range Wireless Motor Drive for Pipeline Transportation
This paper proposes and implements a meter-range wireless motor drive (WMD) system for promising applications of underground pipeline transportations or in-pipe robots.
Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty
To address this, we bridge physics-based state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems.
Rethinking the Spatial Route Prior in Vision-and-Language Navigation
In a most information-rich case of knowing environment maps and admitting shortest-path prior, we observe that given an origin-destination node pair, the internal route can be uniquely determined.
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
It is thus unclear how these algorithms perform on public face hallucination datasets.
Ranked #1 on
Image Super-Resolution
on WLFW
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.
Utterance-level neural confidence measure for end-to-end children speech recognition
The investigation is focused on evaluating and comparing the efficacies of predictor features that are derived from different internal and external modules of the E2E system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Semantic-Preserving Adversarial Text Attacks
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
End2End Occluded Face Recognition by Masking Corrupted Features
However, the state-of-the-art general face recognition models do not generalize well to occluded face images, which are exactly the common cases in real-world scenarios.
SynFace: Face Recognition with Synthetic Data
We then analyze the underlying causes behind the performance gap, e. g., the poor intra-class variations and the domain gap between synthetic and real face images.
Structure-Aware Feature Generation for Zero-Shot Learning
In this paper, we introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to explicitly account for the topological structure in learning both the latent space and the generative networks.
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
On the one hand, CEL blends each embedding with multiple patches of different scales, providing the self-attention module itself with cross-scale features.
Ranked #40 on
Semantic Segmentation
on ADE20K val
Decentralized Federated Learning: Balancing Communication and Computing Costs
The performance of decentralized SGD is jointly influenced by inter-node communications and local updates.
A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing
The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications.
Controlled Caption Generation for Images Through Adversarial Attacks
In contrast, we propose a GAN-based algorithm for crafting adversarial examples for neural image captioning that mimics the internal representation of the CNN such that the resulting deep features of the input image enable a controlled incorrect caption generation through the recurrent network.
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
We trained the model with 10 billion parameters on a 4TB corpus consisting of plain texts and a large-scale knowledge graph.
Robust Pose Transfer with Dynamic Details using Neural Video Rendering
Moreover, we introduce a concise temporal loss in the training stage to suppress the detail flickering that is made more visible due to high-quality dynamic details generated by our method.
Stock Market Analysis with Text Data: A Review
Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models.
Simple Distillation Baselines for Improving Small Self-supervised Models
While large self-supervised models have rivalled the performance of their supervised counterparts, small models still struggle.
Subjective Bias in Abstractive Summarization
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document.
Structure-Regularized Attention for Deformable Object Representation
Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction
Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document.
Attacking Adversarial Attacks as A Defense
In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
A Generative Node-attribute Network Model for Detecting Generalized Structure
Exploring meaningful structural regularities embedded in networks is a key to understanding and analyzing the structure and function of a network.
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features
Social reviews are indispensable resources for modern consumers' decision making.
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter
Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labelling tasks due to their respective strengths.
Joint Face Image Restoration and Frontalization for Recognition
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds
To this end, MorphNet jointly optimizes two objectives for sample-adaptive poisoning: a reconstruction loss that preserves the visual similarity between benign / poisoned point clouds, and a classification loss that enforces a modern recognition model of point clouds tends to mis-classify the poisoned sample to a pre-specified target category.
tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modeling Structure Refinement
Protein structure prediction has been a grand challenge for over 50 years, owing to its broad scientific and application interests.
Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior
We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution
Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure.
Causal factors discovering from Chinese construction accident cases
In China, most of the cases are from accident investigation reports.
Physical world assistive signals for deep neural network classifiers -- neither defense nor attack
We discuss how we can exploit these insights to re-think, or avoid, some patterns that might contribute to, or degrade, the detectability of objects in the real-world.
Improved Matrix Gaussian Mechanism for Differential Privacy
The wide deployment of machine learning in recent years gives rise to a great demand for large-scale and high-dimensional data, for which the privacy raises serious concern.
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way.
COSINE: A Web Server for Clonal and Subclonal Structure Inference and Evolution in Cancer Genomics
In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data.
Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems.
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content.
Generalizing Face Forgery Detection with High-frequency Features
The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
Human-like Controllable Image Captioning with Verb-specific Semantic Roles
However, we argue that almost all existing objective control signals have overlooked two indispensable characteristics of an ideal control signal: 1) Event-compatible: all visual contents referred to in a single sentence should be compatible with the described activity.
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
To this end, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning.
LARNet: Lie Algebra Residual Network for Face Recognition
We prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation.
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning.
Ranked #72 on
Action Recognition
on HMDB-51
Parser-Free Virtual Try-on via Distilling Appearance Flows
A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
Ranked #1 on
Virtual Try-on
on MPV
Token-Modification Adversarial Attacks for Natural Language Processing: A Survey
There are now many adversarial attacks for natural language processing systems.
Learning Discriminative Features using Multi-label Dual Space
We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels.
Analogue cosmological particle creation in an ultracold quantum fluid of light
In inflationary cosmology, the rapid expansion of the early universe resulted in the spontaneous production of cosmological particles from vacuum fluctuations, observable today in the cosmic microwave background anisotropies.
Quantum Gases Optics Quantum Physics
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
A classifier is trained to identify which physician has contoured the CTV from just the contour and corresponding CT scan, to determine if physician styles are consistent and learnable.
Rescattering mechanism of weak decays of double-charm baryons
The doubly charmed baryon $\Xi_{cc}^{++}$ was recently observed by LHCb via the decay processes of $\Xi_{cc}^{++}\to \Lambda_c^+ K^-\pi^+\pi^+$ and $\Xi_c^+\pi^+$.
High Energy Physics - Phenomenology High Energy Physics - Experiment
CPTR: Full Transformer Network for Image Captioning
Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the "words-to-patches" attention in the decoder thanks to the full Transformer architecture.
Phonon Scattering in the Complex Strain Field of a Dislocation
Strain engineering is critical to the performance enhancement of electronic and thermoelectric devices because of its influence on the material thermal conductivity.
Materials Science
Global-Local Propagation Network for RGB-D Semantic Segmentation
Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images.
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control
For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia.
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Adam is one of the most influential adaptive stochastic algorithms for training deep neural networks, which has been pointed out to be divergent even in the simple convex setting via a few simple counterexamples.
Far-Field Super-Resolution Imaging By Nonlinear Excited Evanescent Waves
Abbe's resolution limit, one of the best-known physical limitations, poses a great challenge for any wave systems in imaging, wave transport, and dynamics.
Super-Resolution
Optics
Extremize Optical Chiralities through Polarization Singularities
Chiral optical effects are generally quantified along some specific incident directions of exciting waves (especially for extrinsic chiralities of achiral structures) or defined as direction-independent properties by averaging the responses among all structure orientations.
Optics
Graph Deformer Network
In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.
Pyramid Architecture Search for Real-Time Image Deblurring
Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes.
Adversarial Attack on Deep Cross-Modal Hamming Retrieval
Specifically, given a target model, we first construct its substitute model to exploit cross-modal correlations within hamming space, with which we create adversarial examples by limitedly querying from a target model.
Benchmarking Ultra-High-Definition Image Super-Resolution
Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images.
Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials
Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i. e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices.
Optics
SubICap: Towards Subword-informed Image Captioning
In this work we address this common limitation of IC systems in dealing with rare words in the corpora.
WEmbSim: A Simple yet Effective Metric for Image Captioning
The area of automatic image caption evaluation is still undergoing intensive research to address the needs of generating captions which can meet adequacy and fluency requirements.
LCEval: Learned Composite Metric for Caption Evaluation
Automatic evaluation metrics hold a fundamental importance in the development and fine-grained analysis of captioning systems.
Traffic Assignment Problem for Footpath Networks with Bidirectional Links
The estimation of pedestrian traffic in urban areas is often performed with computationally intensive microscopic models that usually suffer from scalability issues in large-scale footpath networks.
Adversarial Learning for Robust Deep Clustering
Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty
We propose three metrics to determine the proportion of robust images in a dataset and provide scoring to determine the dataset bias.
Towards Dark Jargon Interpretation in Underground Forums
Dark jargons are benign-looking words that have hidden, sinister meanings and are used by participants of underground forums for illicit behavior.
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
Most existing GNN models exploit a single type of aggregator (e. g., mean-pooling) to aggregate neighboring nodes information, and then add or concatenate the output of aggregator to the current representation vector of the center node.
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
In this paper, we propose a novel Proxy-based deep Graph Metric Learning (ProxyGML) approach from the perspective of graph classification, which uses fewer proxies yet achieves better comprehensive performance.
Face Hallucination via Split-Attention in Split-Attention Network
However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).
Probing the Phonon Mean Free Paths in Dislocation Core by Molecular Dynamics Simulation
In this study, the 1D McKelvey-Shockley phonon BTE method was extended to model inhomogeneous materials, where the effect of defects on the phonon MFPs is explicitly obtained.
Materials Science Computational Physics 80A05 I.6.0
Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis
In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks.
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.
Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion
The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers.
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions.
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc.
Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior.
Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos
Inspired by the fact that there exist cross-modal interactions in the human brain, we propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos and thus improve performances on both tasks.
Unsupervised Deep Representation Learning for Real-Time Tracking
The advancement of visual tracking has continuously been brought by deep learning models.
Face Super-Resolution Guided by 3D Facial Priors
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.
Attention-based Residual Speech Portrait Model for Speech to Face Generation
Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face.