Search Results for author:
Found 105 papers, 33 papers with code
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator
To achieve this, we design a contrastive-generator that can effectively generate texts with more attributes.
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm
Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life.
Spectrograms Are Sequences of Patches
Self-supervised pre-training models have been used successfully in several machine learning domains.
Cross Task Neural Architecture Search for EEG Signal Classifications
Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition.
Learning Asynchronous and Error-prone Longitudinal Data via Functional Calibration
In many longitudinal settings, time-varying covariates may not be measured at the same time as responses and are often prone to measurement error.
FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation
We call the proposed framework as FreeSeg, where the mask is freely available from raw feature map of pretraining model.
Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising
How to pair the video ads with the user search is the core task of Baidu video advertising.
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
For this problem, we propose the Explainable Contrastive Language-Image Pre-training (ECLIP), which corrects the explainability via the Masked Max Pooling.
SSORN: Self-Supervised Outlier Removal Network for Robust Homography Estimation
In this paper, we attempt to build a deep learning model that mimics all four steps in the traditional homography estimation pipeline.
CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc.
Online Active Regression
Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels.
Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images
Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
VALHALLA: Visual Hallucination for Machine Translation
In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation
As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community.
The Selectively Adaptive Lasso
In this paper we build upon the theory of HAL to construct the Selectively Adaptive Lasso (SAL), a new algorithm which retains HAL's dimension-free, nonparametric convergence rate but which also scales computationally to massive datasets.
Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates
Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size.
Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression
Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Consequently, we design an optimization model-driven deep CNN in which the unsupervised loss function of the optimization model is enforced on the proposed network for better generalization.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Unveiling Project-Specific Bias in Neural Code Models
Neural code models have introduced significant improvements over many software analysis tasks like type inference, vulnerability detection, etc.
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal
Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components.
Artistic Style Discovery With Independent Components
Style transfer has been well studied in recent years with excellent performance processed.
Improving Video Model Transfer With Dynamic Representation Learning
DRL is then formulated as an adversarial learning problem between the video and spatial models, with the objective of maximizing the dynamic score of learned spatiotemporal classifier.
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
So we try to improve WSSS in the aspect of noise mitigation.
Saliency Detection
Weakly supervised Semantic Segmentation
+1
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning
A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed.
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers.
Ranked #12 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Learning to Cluster via Same-Cluster Queries
We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries.
Inference for High Dimensional Censored Quantile Regression
To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression.
Single Pass Entrywise-Transformed Low Rank Approximation
A natural way to do this would be to simply apply $f$ to each entry of $A$, and then compute the matrix decomposition, but this requires storing all of $A$ as well as multiple passes over its entries.
Deep-learning-based Hyperspectral imaging through a RGB camera
Hyperspectral image (HSI) contains both spatial pattern and spectral information which has been widely used in food safety, remote sensing, and medical detection.
Improvement of image classification by multiple optical scattering
Multiple optical scattering occurs when light propagates in a non-uniform medium.
Cross-Lingual Transfer Learning for Statistical Type Inference
Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data.
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors
Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate.
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.
Learning-Augmented Sketches for Hessians
We show how to design learned sketches for the Hessian in the context of second order methods.
Growth and site-specific organization of micron-scale biomolecular devices on living mammalian cells
Mesoscale molecular assemblies on the cell surface, such as cilia and filopodia, integrate information, control transport and amplify signals.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries
Experimental results demonstrate that the modified Gaussian process regression model considering the battery electrochemical and empirical ageing signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multi-step predictions.
Comments on large central charge $T\bar{T}$ deformed conformal field theory and cutoff AdS holography
For a modified version of cutoff AdS holography which is supposed to work only in the sector of classical pure gravity, we show that the flow equation of the metric and one point function of energy-momentum tensor in $T\bar{T}$ deformation corresponds to the flow equation of the boundary metric and Brown-York tensor on a cutoff surface in AdS space as the cutoff surface moves in the direction of normal geodesics.
High Energy Physics - Theory
Anomalous Hall and Nernst Effects in FeRh
Antiferromagnets with tunable phase transitions are promising for future spintronics applications.
Materials Science
Robust Flat Bands with Tunable Energies in Honeycomb Superlattices
Flat bands in lattice models have provided useful platforms for studying strong correlation and topological physics.
Mesoscale and Nanoscale Physics Strongly Correlated Electrons
Distinguishing limit of Bell states for any $n$-photon $D$-dimensional hyperentanglement
There is a maximum number of distinguished Bell states, i. e. distinguising limit which is very important for increasing the channel capacity of quantum communications.
Quantum Physics
Lets Play Music: Audio-driven Performance Video Generation
We propose a new task named Audio-driven Per-formance Video Generation (APVG), which aims to synthesizethe video of a person playing a certain instrument guided bya given music audio clip.
Learning Representations from Audio-Visual Spatial Alignment
To learn from these spatial cues, we tasked a network to perform contrastive audio-visual spatial alignment of 360{\deg} video and spatial audio.
An Adversarial Attack Defending System for Securing In-Vehicle Networks
In a modern vehicle, there are over seventy Electronics Control Units (ECUs).
Streaming Complexity of SVMs
We show that, for both problems, for dimensions $d=1, 2$, one can obtain streaming algorithms with space polynomially smaller than $\frac{1}{\lambda\epsilon}$, which is the complexity of SGD for strongly convex functions like the bias-regularized SVM, and which is known to be tight in general, even for $d=1$.
Graph Structural-topic Neural Network
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.
Nearly Linear Row Sampling Algorithm for Quantile Regression
We give a row sampling algorithm for the quantile loss function with sample complexity nearly linear in the dimensionality of the data, improving upon the previous best algorithm whose sampling complexity has at least cubic dependence on the dimensionality.
Background Data Resampling for Outlier-Aware Classification
The problem of learning an image classifier that allows detection of out-of-distribution (OOD) examples, with the help of auxiliary background datasets, is studied.
Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling
Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning.
Learning-Augmented Data Stream Algorithms
For estimating the $p$-th frequency moment for $0 < p < 2$ we obtain the first algorithms with optimal update time.
Cosmetic-Aware Makeup Cleanser
Face verification aims at determining whether a pair of face images belongs to the same identity.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
The performance of multi-domain image-to-image translation has been significantly improved by recent progress in deep generative models.
Exploiting Style and Attention in Real-World Super-Resolution
Real-world image super-resolution (SR) is a challenging image translation problem.
Evolving ab initio trading strategies in heterogeneous environments
Securities markets are quintessential complex adaptive systems in which heterogeneous agents compete in an attempt to maximize returns.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
Path Planning Games
Path planning is a fundamental and extensively explored problem in robotic control.
Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning
Eltwise layer is a commonly used structure in the multi-branch deep learning network.
Cross-Spectral Face Hallucination via Disentangling Independent Factors
The cross-sensor gap is one of the challenges that have aroused much research interests in Heterogeneous Face Recognition (HFR).
Data-Driven Neuron Allocation for Scale Aggregation Networks
The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated.
Ranked #589 on
Image Classification
on ImageNet
REPAIR: Removing Representation Bias by Dataset Resampling
An experimental set-up is also introduced to measure the bias of any dataset for a given representation, and the impact of this bias on the performance of recognition models.
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
In this work, we present a novel training framework for GANs, namely biphasic learning, to achieve image-to-image translation in multiple visual domains at $1024^2$ resolution.
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach
In modern biomedical studies, focus has been shifted to estimate and explain the joint effects of high dimensional predictors (for example, molecular biomarkers) on a disease outcome (for example, onset of cancer).
Methodology
Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
Instead of directly regressing the rotation angle of the ellipse as the common practice, GPN represents bounding ellipses as 2D Gaussian distributions on the image plain and minimizes the Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground truth Gaussian for object localization.
A Survey of Deep Facial Attribute Analysis
Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes.
Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning
Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image.
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Our in-depth evaluation on a state-of-the-art speech-to-text DL system demonstrates the effectiveness of our technique in improving quality and reliability of stateful DL systems.
Testing Matrix Rank, Optimally
This improves upon the previous $O(d^2/\epsilon^2)$ bound (SODA'03), and bypasses an $\Omega(d^2/\epsilon^2)$ lower bound of (KDD'14) which holds if the algorithm is required to read a submatrix.
RESOUND: Towards Action Recognition without Representation Bias
The notion of the representation bias of a dataset is proposed to combat this problem.
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Cancer Metastasis Detection With Neural Conditional Random Field
Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality.
Covariance-Insured Screening
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size.
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.
Ranked #1 on
6D Pose Estimation using RGB
on YCB-Video
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment
Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden.
Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification
This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN).
Exploring Neural Transducers for End-to-End Speech Recognition
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition.
Consensus measure of rankings
The consensus measure can be used to evaluate rankings in many information systems, as quite often there is not ground truth available for evaluation.
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.
Ranked #212 on
Object Detection
on COCO test-dev
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision.
Fully Convolutional Instance-aware Semantic Segmentation
It inherits all the merits of FCNs for semantic segmentation and instance mask proposal.
Ranked #84 on
Instance Segmentation
on COCO test-dev
Classification with Ultrahigh-Dimensional Features
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
Ranked #4 on
Real-Time Object Detection
on PASCAL VOC 2007
Instance-sensitive Fully Convolutional Networks
In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances.
Neural Self Talk: Image Understanding via Continuous Questioning and Answering
In this paper we consider the problem of continuously discovering image contents by actively asking image based questions and subsequently answering the questions being asked.
Compositional Memory for Visual Question Answering
We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information.
Free-hand Sketch Synthesis with Deformable Stroke Models
We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category.
Making Better Use of Edges via Perceptual Grouping
We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks.
Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision
The grasp type provides crucial information about human action.
Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features.
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
In this work, we present an efficient and very robust tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object, in a purely online manner.
Random Bits Regression: a Strong General Predictor for Big Data
To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR).
Physical Computing With No Clock to Implement the Gaussian Pyramid of SIFT Algorithm
In this paper, we propose an active circuit network to implement multi-scale Gaussian filter, which is also called Gaussian Pyramid in image preprocessing.
Orientation Robust Text Line Detection in Natural Images
In this paper, higher-order correlation clustering (HOCC) is used for text line detection in natural images.
Convolutional Neural Networks for No-Reference Image Quality Assessment
In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image.
Is Rotation a Nuisance in Shape Recognition?
and 3) how to use rotation unaware local features for rotation aware shape recognition?
Beyond Physical Connections: Tree Models in Human Pose Estimation
Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset.
Learning Visual Symbols for Parsing Human Poses in Images
When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images.
