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Found 371 papers, 160 papers with code
GANE: A Generative Adversarial Network Embedding
Network embedding has become a hot research topic recently which can provide low-dimensional feature representations for many machine learning applications.
On Improving Deep Reinforcement Learning for POMDPs
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e. g., computer Go.
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing
To overcome this weakness, we propose a direct deep learning approach toward image dehazing bypassing the step of transmission map estimation and facilitating end-to-end perceptual optimization.
Weighted Low-Rank Approximation of Matrices and Background Modeling
We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem.
ReHAR: Robust and Efficient Human Activity Recognition
The whole model is trained end-to-end to allow meaningful representations to be generated for the final activity recognition.
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network
Image demosaicing - one of the most important early stages in digital camera pipelines - addressed the problem of reconstructing a full-resolution image from so-called color-filter-arrays.
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
A cascade classifier was designed to efficiently detect adversarials.
Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
We propose to formulate the identification of the lattice groups as a sparse group selection problem.
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves.
Prune the Convolutional Neural Networks with Sparse Shrink
These results have demonstrated the effectiveness of our "Sparse Shrink" algorithm.
FoveaNet: Perspective-aware Urban Scene Parsing
Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors.
Weighted Low Rank Approximation for Background Estimation Problems
Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data.
A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
Principal component pursuit (PCP) is a state-of-the-art approach for background estimation problems.
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations.
Cross-scale predictive dictionaries
Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations.
Video Scene Parsing with Predictive Feature Learning
In this way, the network can effectively learn to capture video dynamics and temporal context, which are critical clues for video scene parsing, without requiring extra manual annotations.
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons
Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model.
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding
State-of-the-art approaches toward image restoration can be classified into model-based and learning-based.
Superimposition-guided Facial Reconstruction from Skull
We develop a new algorithm to perform facial reconstruction from a given skull.
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction
We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences.
Aspect-Based Sentiment Analysis (ABSA)
Multi-Task Learning
+2
Learning Parametric Sparse Models for Image Super-Resolution
More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.
CONet: A Cognitive Ocean Network
The Cognitive Ocean Network (CONet) will become the mainstream of future ocean science and engineering developments.
Adaptive Active Learning for Image Classification
Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis.
Simplified Mirror-Based Camera Pose Computation via Rotation Averaging
Our theoretical contribution extends the applicability of rotation averaging to a more general case, and enables mirror-based pose estimation in closed-form under the chordal L2-metric, or in an outlier-robust way by employing iterative L1-norm averaging.
Object-Aware Dense Semantic Correspondence
To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint.
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising
Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising.
3D Fragment Reassembly Using Integrated Template Guidance and Fracture-Region Matching
This paper studies matching of fragmented objects to recompose their original geometry.
Semi-Supervised Zero-Shot Classification With Label Representation Learning
Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem.
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.
Aligning Users Across Social Networks Using Network Embedding
Li Liu, 1 William K. Cheung, 2 Xin Li, 1⇤ and Lejian Liao1
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.
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation.
STN-Homography: estimate homography parameters directly
In this paper, we introduce the STN-Homography model to directly estimate the homography matrix between image pair.
Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
Medical imaging contains the essential information for rendering diagnostic and treatment decisions.
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.
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.
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.
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.
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.
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization
Image demosaicing and super-resolution are two important tasks in color imaging pipeline.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.
Learning 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.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
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.
Detection of Genuine and Posed Facial Expressions of Emotion: A Review
Facial expressions of emotion play an important role in human social interactions.
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.
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.
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.
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).
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.
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 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.
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.
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.
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
The similarity metric
A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied.
Limitations of Autoregressive Models and Their Alternatives
Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol.
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.
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
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.
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).
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.
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.
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.
Automatic Opioid User Detection From Twitter: Transductive Ensemble Built On Different Meta-graph Based Similarities Over Heterogeneous Information Network
Opioid (e. g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States.
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.
Graph-based Facial Affect Analysis: A Review
As far as we know, this is the first survey of graph-based FAA methods.
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.
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.
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Point clouds have attracted increasing attention.
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.
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).
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.
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.
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.
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.
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.
Ranked #6 on
Table Recognition
on PubTabNet
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective
Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i. e., synthetic distortions) to the target RealSR under the guidance of distortion relation.
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.
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.
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.
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.
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks.
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.
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.
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.
Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide
Our framework is an unsupervised document layout analysis framework.
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).
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.
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.
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.
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.
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.
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.
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.
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.
Multi-Object Tracking Meets Moving UAV
Multi-object tracking in unmanned aerial vehicle (UAV) videos is an important vision task and can be applied in a wide range of applications.
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae.
Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment
At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition
Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence.
Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment
Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA.
Blind Image Quality Assessment
Unsupervised Domain Adaptation
Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing
Instead, we propose point cloud attacks from a new perspective -- the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that corresponds to varying certain geometric structure.
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Specifically, we find a more compact and reliable space i. e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i. e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA.
Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression
Our proposed transcoding architecture shows significant superiority in the compression of JPEG images thanks to the collaboration of learned lossy transform coding and residual entropy coding.
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
Large-step neural network for learning the symplectic evolution from partitioned data
Based on Chen & Tao (2021), the symplectic mapping is represented by a generating function.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets.
Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification
Learning a robust and discriminative feature representation is a crucial challenge for object ReID.
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
To this end, we propose Flow2Flow, a unified framework that could jointly achieve training sample expansion and cross-modality image generation for V2I person ReID.
Toward an Over-parameterized Direct-Fit Model of Visual Perception
In this paper, we revisit the problem of computational modeling of simple and complex cells for an over-parameterized and direct-fit model of visual perception.
Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network
We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension.
Cutting-Splicing data augmentation: A novel technology for medical image segmentation
Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks.
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
Development of a Hybrid Simulation and Experiment Test Platform for Dynamic Positioning Vessels
The test platform can test the performance of DP system and determine the operational time window.
Deep Learning-Based Channel Estimation for Double-RIS Aided Massive MIMO System
Reconfigurable Intelligent Surface (RIS) is considered as an energy-efficient solution for future wireless communication networks due to its fast and low-cost configuration.
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
This provides an opportunity for the defenders (i. e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e. g., source code view in addition to the binary view).
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection
While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as.
Batch-based Model Registration for Fast 3D Sherd Reconstruction
In this work, we aim to develop a portable, high-throughput, and accurate reconstruction system for efficient digitization of fragments excavated in archaeological sites.
Transformation-Equivariant 3D Object Detection for Autonomous Driving
However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed.
Learning Compact Features via In-Training Representation Alignment
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i. e., last hidden layer) and a linear classifier (i. e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e. g., cross-entropy).
AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer
Then, in \textbf{AdaCM}, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair.
Coarse-to-Fine Contrastive Learning on Graphs
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently.
PointSmile: Point Self-supervised Learning via Curriculum Mutual Information
From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i. e., starting with an easy curriculum and gradually increasing the difficulty of that curriculum.
Analysis of Biomass Sustainability Indicators from a Machine Learning Perspective
This study proposes a robust model for biomass sustainability prediction by analyzing sustainability indicators using machine learning models.
MorphGANFormer: Transformer-based Face Morphing and De-Morphing
Semantic face image manipulation has received increasing attention in recent years.
Toward a Geometric Theory of Manifold Untangling
It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling.
Toward NeuroDM: Where Computational Neuroscience Meets Data Mining
At the intersection of computational neuroscience (CN) and data mining (DM), we advocate a holistic view toward their rich connections.
Emotional Reaction Intensity Estimation Based on Multimodal Data
In this paper, 1. better features are extracted with the SOTA pretrained models.
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community.
MobileInst: Video Instance Segmentation on the Mobile
To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA
Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them.
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis
The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss.
CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes.
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis
How can we teach a computer to recognize 10, 000 different actions?
MEDIC: A Multimodal Empathy Dataset in Counseling
We also propose three labels (i. e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients.
UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery
Experimental results on the re-annotated Place Pulse 2. 0 dataset demonstrate promising detection performance of the proposed method, with an accuracy of 79. 9%.
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data.
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
Vector Quantization With Self-Attention for Quality-Independent Representation Learning
During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images.
Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring
To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.
Two-Stream Regression Network for Dental Implant Position Prediction
In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue.
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
A2B: Anchor to Barycentric Coordinate for Robust Correspondence
This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences.
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework
Our proposed method can simultaneously (1) secure VARS from adversarial attacks characterized by local perturbations by image reconstruction based on global vision transformers; and (2) accurately detect adversarial examples using a novel contrastive learning approach.
TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction
When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available.
Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media
By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection.
Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learning
Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects.
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
A method for measuring the synthesis complexity is proposed to automatically determine the synthesis order in our sequential GAN.
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
In this paper, we analyze the convergence of RK for noisy linear systems when the coefficient matrix, $A$, is corrupted with both additive and multiplicative noise, along with the noisy vector, $b$.
VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation
In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion.
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective
Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment.
Blind Image Quality Assessment
Unsupervised Domain Adaptation
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient.
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning
Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices.
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations
Imitation Learning (IL) aims to discover a policy by minimizing the discrepancy between the agent's behavior and expert demonstrations.
Towards Control-Centric Representations in Reinforcement Learning from Images
Image-based Reinforcement Learning is a practical yet challenging task.
Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation
Overhead line inspection greatly benefits from defect recognition using visible light imagery.
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield.
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.
Brainformer: Modeling MRI Brain Functions to Machine Vision
In this work, we first present a simple yet effective Brainformer approach, a novel Transformer-based framework, to analyze the patterns of fMRI in the human perception system from the machine learning perspective.
Cross-BERT for Point Cloud Pretraining
By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks.
Disentangled Clothed Avatar Generation from Text Descriptions
In this paper, we introduced a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar.
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
Furthermore, a temporal transformer is utilized for spatiotemporal feature fusion across the video.
Spectrum-guided Feature Enhancement Network for Event Person Re-Identification
This network consists of two innovative components: the Multi-grain Spectrum Attention Mechanism (MSAM) and the Consecutive Patch Dropout Module (CPDM).
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Adversarial Malware Generation (AMG), the gen- eration of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense.
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model.
On Organizational Principles of Neural Systems
Inspired by classical embodied cognition and the emerging multimodal interaction, we study the organizational principles of neural systems at three levels (device/implementation, circuit/algorithm, and system/computational) in this survey paper.
Neural Radiance Fields in Medical Imaging: Challenges and Next Steps
Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data.
