Search Results for author:
Found 164 papers, 48 papers with code
Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Delay-Doppler Waveforms in Doubly Selective Channels
Delay-Doppler waveform design has been considered as a promising solution to achieve reliable communication under high-mobility channels for the space-air-ground-integrated networks (SAGIN).
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).
XFL: A High Performace, Lightweighted Federated Learning Framework
This paper introduces XFL, an industrial-grade federated learning project.
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
Reachability Analysis of Neural Network Control Systems
We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model.
APAC: Authorized Probability-controlled Actor-Critic For Offline Reinforcement Learning
Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points.
Dynamic MLP for MRI Reconstruction
We further compared the proposed dMLP with CNNs using large kernels and studied pure MLP-based reconstruction using a stack of 1D dMLPs, as well as its CNN counterpart using only 1D convolutions.
BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously.
Semantics-Preserving Sketch Embedding for Face Generation
With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images.
Hierarchical Normalization for Robust Monocular Depth Estimation
In this paper, we address monocular depth estimation with deep neural networks.
Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification
However, training two networks with a set of noisy pseudo labels reduces the complementarity of the two networks and results in label noise accumulation.
On the Learning Mechanisms in Physical Reasoning
Taken together, the results on the challenging benchmark of PHYRE show that LfI is, if not better, as good as LfD for dynamics prediction.
Infrared: A Meta Bug Detector
The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors.
MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equations
To fill the gaps above, we propose three initiatives in this paper: (1) A Multi-Receptive-Field PINN (MRF-PINN) model is established to solve different types of PDEs on various mesh resolutions without manual tuning; (2) The dimensional balance method is used to estimate the loss weights when solving Navier-Stokes equations; (3) The Taylor polynomial is used to pad the virtual nodes near the boundaries for implementing high-order finite difference.
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning
In Multi-Agent Reinforcement Learning, communication is critical to encourage cooperation among agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images.
KD-MVS: Knowledge Distillation Based Self-supervised Learning for MVS
Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth.
Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations.
Few-shot Open-set Recognition Using Background as Unknowns
First, instead of learning the decision boundaries between seen classes, as is done in standard close-set classification, we reserve space for unseen classes, such that images located in these areas are recognized as the unseen classes.
A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines
Evaluations through simulation and on real IBM-Q devices show that our framework can significantly reduce the error rate by up to 6$\times$, with only $\sim$60\% circuit depth compared to state-of-the-art gate scheduling approaches.
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
This technical report presents an effective method for motion prediction in autonomous driving.
Ranked #11 on
Motion Forecasting
on Argoverse CVPR 2020
Automatic Generation of Product-Image Sequence in E-commerce
For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
EST: Evaluating Scientific Thinking in Artificial Agents
Such inefficacy of learning in scientific thinking calls for future research in building humanlike intelligence.
DETR++: Taming Your Multi-Scale Detection Transformer
Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12].
On the Perils of Cascading Robust Classifiers
We present \emph{cascade attack} (CasA), an adversarial attack against cascading ensembles, and show that: (1) there exists an adversarial input for up to 88\% of the samples where the ensemble claims to be certifiably robust and accurate; and (2) the accuracy of a cascading ensemble under our attack is as low as 11\% when it claims to be certifiably robust and accurate on 97\% of the test set.
Multi-agent Databases via Independent Learning
Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?".
Constrained Reinforcement Learning for Short Video Recommendation
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.
MPI: Evaluating and Inducing Personality in Pre-trained Language Models
Further, given this evaluation framework, (3) how can we induce a certain personality in a fully controllable fashion?
Correction of out-of-focus microscopic images by deep learning
Results To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function.
Efficient Few-Shot Object Detection via Knowledge Inheritance
Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed.
Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction
In this paper, we study the interaction between pedestrians and vehicles and propose a novel neural network structure called the Pedestrian-Vehicle Interaction (PVI) extractor for learning the pedestrian-vehicle interaction.
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior
In this work, we focus on scene flow learning on point clouds in a self-supervised manner.
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain.
Contrastive Learning
Domain Adaptive Person Re-Identification
+2
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization
We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product titleand review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fitswell for fine-tuning tasks by combining input and output all to-gether.
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning
Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization.
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Model smoothing is of central importance for obtaining a reliable teacher model in the student-teacher framework, where the teacher generates surrogate supervision signals to train the student.
Effectiveness of Optimization Algorithms in Deep Image Classification
Adam is applied widely to train neural networks.
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations
Current implementations exhibit poor performance due to challenges such as irregular memory accesses and thread-level synchronization overheads on CPU.
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning
This leads to large overestimations of the Q values and performance deterioration of the learned policy.
Degradation Attacks on Certifiably Robust Neural Networks
Certifiably robust neural networks employ provable run-time defenses against adversarial examples by checking if the model is locally robust at the input under evaluation.
Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph
In this manuscript, we introduce a semi-automatic scene graph annotation tool for images, the GeneAnnotator.
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.
Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development
Our A/B testing and pilot results demonstrated the HITL pipeline can improve annotation speed and capacity by at least 80% and quality is comparable to or higher than manual double pass annotation.
Calibrating Class Activation Maps for Long-Tailed Visual Recognition
First, we present a Class Activation Map Calibration (CAMC) module to improve the learning and prediction of network classifiers, by enforcing network prediction based on important image regions.
Binocular Mutual Learning for Improving Few-shot Classification
From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view.
Temporal Knowledge Consistency for Unsupervised Visual Representation Learning
The instance discrimination paradigm has become dominant in unsupervised learning.
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community.
Ranked #3 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Few-shot Segmentation with Optimal Transport Matching and Message Flow
In this work, we argue that every support pixel's information is desired to be transferred to all query pixels and propose a Correspondence Matching Network (CMNet) with an Optimal Transport Matching module to mine out the correspondence between the query and support images.
Unified Regularity Measures for Sample-wise Learning and Generalization
Fundamental machine learning theory shows that different samples contribute unequally both in learning and testing processes.
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors.
Domain Adaptive Person Re-Identification
Person Re-Identification
M2IOSR: Maximal Mutual Information Open Set Recognition
In this work, we aim to address the challenging task of open set recognition (OSR).
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
SNALS captures the joint interactions of a hyperedge by its local environment, which is retrieved by collecting the spectrum information of their connections.
Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios
In this paper, we use the recently released large-scale Waymo Open Dataset in urban traffic scenarios, which includes 374 urban training scenes and 76 urban testing scenes to analyze the performance of our proposed algorithm in comparison to the state-of-the-art (SOTA) models.
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation
Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.
Few-Shot Incremental Learning with Continually Evolved Classifiers
First, we adopt a simple but effective decoupled learning strategy of representations and classifiers that only the classifiers are updated in each incremental session, which avoids knowledge forgetting in the representations.
Ranked #5 on
Few-Shot Class-Incremental Learning
on mini-Imagenet
Efficient DETR: Improving End-to-End Object Detector with Dense Prior
By taking advantage of both dense detection and sparse set detection, Efficient DETR leverages dense prior to initialize the object containers and brings the gap of the 1-decoder structure and 6-decoder structure.
Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution
To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner; central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects.
ACRE: Abstract Causal REasoning Beyond Covariation
Causal induction, i. e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data.
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance
Predicting agents' future trajectories plays a crucial role in modern AI systems, yet it is challenging due to intricate interactions exhibited in multi-agent systems, especially when it comes to collision avoidance.
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
Ranked #2 on
Metric Learning
on DyML-Animal
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement
The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i. e. style feature, and the feature representing the invariant semantic content, i. e. content feature.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.
Ranked #10 on
Few-Shot Object Detection
on MS-COCO (30-shot)
End-to-End Human Object Interaction Detection with HOI Transformer
We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner.
Ranked #20 on
Human-Object Interaction Detection
on HICO-DET
(using extra training data)
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations
Although deep learning (DL) has received much attention in accelerated MRI, recent studies suggest small perturbations may lead to instabilities in DL-based reconstructions, leading to concern for their clinical application.
Nanoscale magnetization and current imaging using scanning-probe magneto-thermal microscopy
In addition, we study the near-field and time-resolved characteristics of our signal and find that our instrument possesses a spatial resolution on the scale of 100 nm and a temporal resolution below 100 ps.
Mesoscale and Nanoscale Physics Materials Science
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Concretely, STAR has the star topology, which consists of the shared centered parameters and domain-specific parameters.
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
However, dammed basins must be present in the training dataset.
Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification
At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC.
CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images.
Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning
We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.
The Unreasonable Effectiveness of the Class-reversed Sampling in Tail Sample Memorization
Long-tailed visual class recognition poses significant challenges to traditional machine learning and emerging deep networks due to its inherent class imbalance.
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
In this work, we first organize the general MLLRR problem into three subproblems based on different low rank properties , and we argue that most of existing efforts focus on only one category, which leaves the other two unsolved.
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning
The goal of Offline Reinforcement Learning (RL) is to address this problem by learning effective policies using previously collected datasets.
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation
Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission
To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices.
Exploring the many-body dynamics near a conical intersection with trapped Rydberg ions
Conical intersections between electronic potential energy surfaces are paradigmatic for the study of non-adiabatic processes in the excited states of large molecules.
Atomic Physics Quantum Physics
Learning Disentangled Representations of Videos with Missing Data
Missing data poses significant challenges while learning representations of video sequences.
Manual-Label Free 3D Detection via An Open-Source Simulator
In this paper, we propose a manual-label free 3D detection algorithm that leverages the CARLA simulator to generate a large amount of self-labeled training samples and introduces a novel Domain Adaptive VoxelNet (DA-VoxelNet) that can cross the distribution gap from the synthetic data to the real scenario.
Matched Queues with Matching Batch Pair (m, n)
In this paper, we discuss an interesting but challenging bilateral stochastically matching problem: A more general matched queue with matching batch pair (m, n) and two types (i. e., types A and B) of impatient customers, where the arrivals of A- and B-customers are both Poisson processes, m A-customers and n B-customers are matched as a group which leaves the system immediately, and the customers' impatient behavior is to guarantee the stability of the system.
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining.
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory
With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i. e.}, deep image classification and deep metric learning (on long-tailed data).
Ranked #34 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
Open Set Recognition with Conditional Probabilistic Generative Models
A typical challenge that hinders their real-world applications is that unknown samples may be fed into the system during the testing phase, but traditional deep neural networks will wrongly recognize these unknown samples as one of the known classes.
Denoising individual bias for a fairer binary submatrix detection
Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications.
Geometric All-Way Boolean Tensor Decomposition
Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains.
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance.
Supervised clustering of high dimensional data using regularized mixture modeling
Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease.
Weight-dependent Gates for Network Pruning
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint.
Learning Disentangled Representations of Video with Missing Data
Missing data poses significant challenges while learning representations of video sequences.
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Furthermore, we propose to generate \emph{diverse} model rollouts by non-uniform sampling of the environment states such that the entropy of the model rollouts is maximized.
Model-based Reinforcement Learning
reinforcement-learning
+1
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers
We adopt the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
Component-wise Adaptive Trimming For Robust Mixture Regression
Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination.
Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning
To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG).
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies.
CRNet: Cross-Reference Networks for Few-Shot Segmentation
In this paper, we propose a cross-reference network (CRNet) for few-shot segmentation.
Conditional Gaussian Distribution Learning for Open Set Recognition
A typical challenge is that unknown samples may be fed into the system during the testing phase and traditional deep neural networks will wrongly recognize the unknown sample as one of the known classes.
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
BigGAN-based Bayesian reconstruction of natural images from human brain activity
In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of optical flow.
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification
Another challenge of RGB-infrared ReID is that the intra-person (images from the same person) discrepancy is often larger than the inter-person (images from different persons) discrepancy, so a dual-subspace pairing strategy is proposed to alleviate this problem.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Collaborative Inference for Efficient Remote Monitoring
While current machine learning models have impressive performance over a wide range of applications, their large size and complexity render them unsuitable for tasks such as remote monitoring on edge devices with limited storage and computational power.
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge.
Learning Perceptual Inference by Contrasting
"Thinking in pictures," [1] i. e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a crucial factor in the intellectual history of technology development.
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration.
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Safety-aware exploration is another challenge in real systems since certain actions at particular states may result in catastrophic outcomes.
Model-based Reinforcement Learning
reinforcement-learning
+4
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation
One-shot image segmentation aims to undertake the segmentation task of a novel class with only one training image available.
Ranked #62 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Long-term planning, short-term adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
EPOSIT: An Absolute Pose Estimation Method for Pinhole and Fish-Eye Cameras
This paper presents a generic 6DOF camera pose estimation method, which can be used for both the pinhole camera and the fish-eye camera.
Re-ID Driven Localization Refinement for Person Search
The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task.
Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter
Another way to remove that limitation, an optional classification layer, trained on manually annotated DoS attack tweets, to filter out non-attack tweets can be used to increase precision at the expense of recall.
Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation
Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples.
Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN
A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels.
Computational Physics Materials Science Applied Physics
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.
Distributed Optimization for Over-Parameterized Learning
Moreover, we show that the more local updating can reduce the overall communication, even for an infinity number of steps where each node is free to update its local model to near-optimality before exchanging information.
Bimodal Stereo: Joint Shape and Pose Estimation from Color-Depth Image Pair
In the pipeline, the estimated shape is refined by the shape prior from the given depth map under the estimated pose.
Joint haze image synthesis and dehazing with mmd-vae losses
Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs.
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention.
Ranked #21 on
Visual Question Answering (VQA)
on MSVD-QA
Re-Identification Supervised Texture Generation
The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years.
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks.
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images).
Ranked #14 on
Person Re-Identification
on Market-1501-C
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively.
Neurons and Cognition
STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID.
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
In this work, we propose a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation.
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets.
Ranked #65 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
A visual encoding model based on deep neural networks and transfer learning
In this framework, we employ the transfer learning technique to incorporate a pre-trained DNN (i. e., AlexNet) and train a nonlinear mapping from visual features to brain activity.
A Top-down Approach to Articulated Human Pose Estimation and Tracking
Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging.
Differentially Private ADMM for Distributed Medical Machine Learning
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests.
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images.
Fast Botnet Detection From Streaming Logs Using Online Lanczos Method
Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc.
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a pre-specified time window.
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method.
SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress.
Deep Learning Bandgaps of Topologically Doped Graphene
Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales.
Materials Science Computational Physics
Vector Learning for Cross Domain Representations
We leverage a sequence-to-sequence model to generate synthetic captions that have the same meaning for having a robust image generation.
Semantically Invariant Text-to-Image Generation
Along with MMVR, we propose two improvements to the text conditioned image generation.
Semantic Sentence Embeddings for Paraphrasing and Text Summarization
This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing.
Batch-normalized Recurrent Highway Networks
Gradient control plays an important role in feed-forward networks applied to various computer vision tasks.
A Coarse-To-Fine Framework For Video Object Segmentation
In this study, we develop an unsupervised coarse-to-fine video analysis framework and prototype system to extract a salient object in a video sequence.
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention
Video-based person re-identification (ReID) is a challenging problem, where some video tracks of people across non-overlapping cameras are available for matching.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
DenseASPP for Semantic Segmentation in Street Scenes
To this end, we propose Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a set of atrous convolutional layers in a dense way, such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size.
Ranked #5 on
Semantic Segmentation
on SkyScapes-Dense
Learning with Non-Convex Truncated Losses by SGD
Learning with a {\it convex loss} function has been a dominating paradigm for many years.
Adaptive Recurrent Neural Network Based on Mixture Layer
By leveraging the mixture layer, the proposed method can adaptively update states according to the similarities between encoded inputs and prototype vectors, leading to a stronger capacity in assimilating sequences with multiple patterns.
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN).
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner.
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention.
SOT for MOT
Another contribution is that we show with a deep learning based appearance model, it is easy to associate detections of the same object efficiently and also with high accuracy.
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features.
Ranked #1 on
Person Re-Identification
on CUHK-SYSU
Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
Person re-identification (ReID) is an important task in computer vision.
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process.
Efficient Eye Typing with 9-direction Gaze Estimation
According to the results from our experiments, our CNN model is able to accurately estimate different people's gaze under various lighting conditions by different devices.
Saliency Detection by Forward and Backward Cues in Deep-CNNs
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not.
Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning
The challenges in this task are the lexical gaps between questions for the word ambiguity and word mismatch problem.
Cross Domain Knowledge Transfer for Person Re-identification
Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another.
Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency
In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i. e. silhouette consistency, into consideration.
Input Aggregated Network for Face Video Representation
To sufficiently discover the useful information contained in face videos, we present a novel network architecture called input aggregated network which is able to learn fixed-length representations for variable-length face videos.
Query Adaptive Similarity Measure for RGB-D Object Recognition
The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
We present a novel global stereo model designed for view interpolation.
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
