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Found 204 papers, 78 papers with code
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features of human actions from their skeletal representations.
Aspect-based Sentiment Analysis in Question Answering Forums
This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair.
A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding
However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e. g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters.
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.
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.
Relational Representation Learning in Visually-Rich Documents
To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views.
Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions
In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools.
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
Predicting gene functions is a challenge for biologists in the post genomic era.
The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the multimedia community, owing to the excellent ability of learning visual representation from unlabeled data.
DR-GAN: Distribution Regularization for Text-to-Image Generation
This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning.
Context-aware Visual Tracking with Joint Meta-updating
These deep trackers usually do not perform online update or update single sub-branch of the tracking model, for which they cannot adapt to the appearance variation of objects.
Unsupervised Learning of Accurate Siamese Tracking
As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward.
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results.
Ranked #1 on
Action Recognition
on Jester
Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel.
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks.
Model Attribution of Face-swap Deepfake Videos
Then we take Deepfakes model attribution as a multiclass classification task and propose a spatial and temporal attention based method to explore the differences among Deepfakes in the new dataset.
Low-Rank Phase Retrieval with Structured Tensor Models
We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signals.
Enhancing Cross-lingual Prompting with Mask Token Augmentation
Prompting shows promising results in few-shot scenarios.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers.
A multi-domain virtual network embedding algorithm with delay prediction
Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN).
Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide
Our framework is an unsupervised document layout analysis framework.
Machine learning prediction for mean motion resonance behaviour -- The planar case
Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem.
A Survey on Applications of Digital Human Avatars toward Virtual Co-presence
This paper investigates different approaches to build and use digital human avatars toward interactive Virtual Co-presence (VCP) environments.
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
Robust Depth Completion with Uncertainty-Driven Loss Functions
In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion.
An Informative Tracking Benchmark
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming.
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks.
Uncertainty-Driven Loss for Single Image Super-Resolution
Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously.
Interactive Model with Structural Loss for Language-based Abductive Reasoning
Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called ``joint softmax focal loss'' in this paper.
Document Layout Analysis with Aesthetic-Guided Image Augmentation
To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.
Neural Collaborative Graph Machines for Table Structure Recognition
We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs.
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.
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition
For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks.
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.
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
Knowledge Based Multilingual Language Model
Knowledge enriched language representation learning has shown promising performance across various knowledge-intensive NLP tasks.
Internationalizing AI: Evolution and Impact of Distance Factors
A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i. e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed.
Deep Models with Fusion Strategies for MVP Point Cloud Registration
The main goal of point cloud registration in Multi-View Partial (MVP) Challenge 2021 is to estimate a rigid transformation to align a point cloud pair.
Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem model
In this study, we extend a deep learning (DL) model, which could predict the heave and surge motions of a floating semi-submersible 20 to 50 seconds ahead with good accuracy, to quantify its uncertainty of the predictive time series with the help of the dropout technique.
Vector-quantized Image Modeling with Improved VQGAN
Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively.
Aspect Sentiment Quad Prediction as Paraphrase Generation
Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity.
Multilingual AMR Parsing with Noisy Knowledge Distillation
We study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher.
Characterizing interdisciplinarity in drug research: a translational science perspective
Despite the significant advances in life science, it still takes decades to translate a basic drug discovery into a cure for human disease.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach
Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging.
Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion
Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG).
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.
Ranked #1 on
Object Tracking
on VisEvent
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks.
Ranked #1 on
Object Detection
on A2D
Saliency-Associated Object Tracking
A potential limitation of such trackers is that not all patches are equally informative for tracking.
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.
Towards Generative Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has received increasing attention recently.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect-Based Sentiment Analysis
Aspect Sentiment Triplet Extraction
+1
Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visible
Replacing electrons with photons is a compelling route towards light-speed, highly parallel, and low-power artificial intelligence computing.
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Point clouds have attracted increasing attention.
Self-Supervised Tracking via Target-Aware Data Synthesis
While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training.
Probabilistic Model Distillation for Semantic Correspondence
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
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.
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead.
Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness
Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial.
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.
SiamCorners: Siamese Corner Networks for Visual Tracking
By tracking a target as a pair of corners, we avoid the need to design the anchor boxes.
Learning Semantic Person Image Generation by Region-Adaptive Normalization
In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).
Searching Efficient Model-guided Deep Network for Image Denoising
Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.
Mutual Graph Learning for Camouflaged Object Detection
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
A Detector-oblivious Multi-arm Network for Keypoint Matching
This paper presents a matching network to establish point correspondence between images.
Graph-based Facial Affect Analysis: A Review
As far as we know, this is the first survey of graph-based FAA methods.
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.
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.
Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models
Driven by the tremendous effort in researching novel deep learning (DL) algorithms, the training cost of developing new models increases staggeringly in recent years.
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control
Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively.
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo
Photometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions.
Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distance
This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance.
Recent Advances of Generic Object Detection with Deep Learning: A Review
Object detection is an important and challenging problem in computer vision.
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.
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints
This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness.
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.
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events
To date, machine learning for human action recognition in video has been widely implemented in sports activities.
Magnetoelectric coupling and decoupling in multiferroic hexagonal YbFeO3 thin films
The coupling between ferroelectric and magnetic orders in multiferroic materials and the nature of magnetoelectric (ME) effects are enduring experimental challenges.
Materials Science Other Condensed Matter
Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model
To address this problem, we here propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views.
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond
Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach, which have been studied extensively on the sequence-level tasks.
Limitations of Autoregressive Models and Their Alternatives
Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol.
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
We also design a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions.
Ranked #26 on
Semi-Supervised Video Object Segmentation
on YouTube-VOS 2018 val
(using extra training data)
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution
Thanks to newly designed Deformable Kernel Convolution Alignment (DKC_Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can better exploit both spatial and temporal redundancies to facilitate the information propagation across different layers.
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.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Enhancing Dialogue Generation via Multi-Level Contrastive Learning
Most of the existing works for dialogue generation are data-driven models trained directly on corpora crawled from websites.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR).
Efficiency in Real-time Webcam Gaze Tracking
To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration.
Detection of Genuine and Posed Facial Expressions of Emotion: A Review
Facial expressions of emotion play an important role in human social interactions.
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set.
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Thus, the global policy of the whole page could be sub-optimal.
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.
MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification
This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #4 on
RGB-D Salient Object Detection
on NJU2K
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Multi-node Bert-pretraining: Cost-efficient Approach
As a result, to train these models within a reasonable time, machine learning (ML) programmers often require advanced hardware setups such as the premium GPU-enabled NVIDIA DGX workstations or specialized accelerators such as Google's TPU Pods.
Predicting heave and surge motions of a semi-submersible with neural networks
With the help of measured waves, the prediction extended 46. 5 s into future with an average accuracy close to 90%.
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.
Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items.
Defending against adversarial attacks on medical imaging AI system, classification or detection?
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems.
Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving.
Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic
The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials.
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.
3D Face Anti-spoofing with Factorized Bilinear Coding
We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years.
Training Recurrent Neural Networks Online by Learning Explicit State Variables
Recurrent neural networks (RNNs) allow an agent to construct a state-representation from a stream of experience, which is essential in partially observable problems.
Context-aware Helpfulness Prediction for Online Product Reviews
In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review.
A Chinese Corpus for Fine-grained Entity Typing
In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4, 800 mentions manually labeled through crowdsourcing.
Leveraging Planar Regularities for Point Line Visual-Inertial Odometry
To improve the accuracy of 3D mesh generation and localization, we propose a tightly-coupled monocular VIO system, PLP-VIO, which exploits point features and line features as well as plane regularities.
Towards Evaluating the Robustness of Chinese BERT Classifiers
Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks.
COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up.
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations.
Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States
Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision.
Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests
A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users.
Improve SGD Training via Aligning Mini-batches
In each iteration of SGD, a mini-batch from the training data is sampled and the true gradient of the loss function is estimated as the noisy gradient calculated on this mini-batch.
A Real-Time Deep Network for Crowd Counting
Automatic analysis of highly crowded people has attracted extensive attention from computer vision research.
Exploiting Semantic Relations for Fine-grained Entity Typing
We find that, in some cases, existing neural fine-grained entity typing models may ignore the semantic information in the context that is important for typing.
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features.
Face Beautification: Beyond Makeup Transfer
Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework of face beautification.
Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image
We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image.
Relevance-Promoting Language Model for Short-Text Conversation
In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation.
Multi-Task Driven Feature Models for Thermal Infrared Tracking
These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
Sparse estimation via $\ell_q$ optimization method in high-dimensional linear regression
In this paper, we discuss the statistical properties of the $\ell_q$ optimization methods $(0<q\leq 1)$, including the $\ell_q$ minimization method and the $\ell_q$ regularization method, for estimating a sparse parameter from noisy observations in high-dimensional linear regression with either a deterministic or random design.
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization
Image demosaicing and super-resolution are two important tasks in color imaging pipeline.
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology
To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis.
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.
Aspect-Based Sentiment Analysis
Unsupervised Domain Adaptation
GRIP: Graph-based Interaction-aware Trajectory Prediction
The prediction error of GRIP is one meter shorter than existing schemes.
Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks
In this work, the face-centered cubic (fcc) anion frameworks were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion migration.
Applied Physics
Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net
The clinical treatment of degenerative and developmental lumbar spinal stenosis (LSS) is different.
Spoofing and Anti-Spoofing with Wax Figure Faces
In this work, we introduce a wax figure face database (WFFD) as a novel and super-realistic 3D face presentation attack.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e. g. BERT, on the E2E-ABSA task.
Improving Fine-grained Entity Typing with Entity Linking
Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention.
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
Small and Practical BERT Models for Sequence Labeling
We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU.
Deep Concept-wise Temporal Convolutional Networks for Action Localization
In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.
Domain-adversarial Network Alignment
Network alignment is a critical task to a wide variety of fields.
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
To address these difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate confidence scores of densely distributed proposals, which denote a proposal as a matching pair of starting and ending boundaries and combine all densely distributed BM pairs into the BM confidence map.
Ranked #1 on
Action Recognition
on THUMOS’14
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied.
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.
Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
Medical imaging contains the essential information for rendering diagnostic and treatment decisions.
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
STN-Homography: estimate homography parameters directly
In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair.
RF-Net: An End-to-End Image Matching Network based on Receptive Field
This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images.
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation.
Target-Aware Deep Tracking
Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision.
Pyramid Mask Text Detector
Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically.
Ranked #1 on
Scene Text Detection
on ICDAR 2017 MLT
Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment
Despite the significant advances in iris segmentation, accomplishing accurate iris segmentation in non-cooperative environment remains a grand challenge.
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
It prunes the architecture search space with a partial order assumption to automatically search for the architectures with the best speed and accuracy trade-off.
CONet: A Cognitive Ocean Network
The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments.
DAC: Data-free Automatic Acceleration of Convolutional Networks
A complex deep learning model with high accuracy runs slowly on resource-limited devices, while a light-weight model that runs much faster loses accuracy.
