Search Results for author:
Found 90 papers, 25 papers with code
The Noisy-Logical Distribution and its Application to Causal Inference
We describe a novel noisy-logical distribution for representing the distribution of a binary output variable conditioned on multiple binary input variables.
Model selection and velocity estimation using novel priors for motion patterns
Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation.
Modeling the spacing effect in sequential category learning
We develop a Bayesian sequential model for category learning.
Gaussian sampling by local perturbations
We present a technique for exact simulation of Gaussian Markov random fields (GMRFs), which can be interpreted as locally injecting noise to each Gaussian factor independently, followed by computing the mean/mode of the perturbed GMRF.
A unified model of short-range and long-range motion perception
The human vision system is able to effortlessly perceive both short-range and long-range motion patterns in complex dynamic scenes.
Functional form of motion priors in human motion perception
We then measured human performance for motion tasks and found that we obtained better fit for the L1-norm (Laplace) than for the L2-norm (Gaussian).
Complexity of Representation and Inference in Compositional Models with Part Sharing
This paper describes serial and parallel compositional models of multiple objects with part sharing.
Robust Estimation of Nonrigid Transformation for Point Set Registration
In the second step, we estimate the transformation using a robust estimator called L 2 E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step.
Robust Region Grouping via Internal Patch Statistics
In this work, we present an efficient multi-scale low-rank representation for image segmentation.
An Approach to Pose-Based Action Recognition
We start by improving a state of the art method for estimating human joint locations from videos.
Single Image Super-resolution using Deformable Patches
We proposed a deformable patches based method for single image super-resolution.
Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision
Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a budget of tens or hundreds of thousands of dollars.
Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting.
Robust Estimation of 3D Human Poses from a Single Image
We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons.
Ranked #27 on
3D Human Pose Estimation
on HumanEva-I
The Secrets of Salient Object Segmentation
The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.
Learning Deep Structured Models
Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials.
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the ``positiveness" instead of a binary label of each training sample is available.
Representing Data by a Mixture of Activated Simplices
We show that under this restriction, building a model with simplices amounts to constructing a convex hull inside the sphere whose boundary facets is close to the data.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
This is due to the very invariance properties that make DCNNs good for high level tasks.
Ranked #3 on
Scene Segmentation
on SUN-RGBD
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution
Based on the fact that singular primitive patches are more invariant to the scale change (i. e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation.
Region-Based Temporally Consistent Video Post-Processing
We study the problem of temporally consistent video post-processing.
Towards Unified Depth and Semantic Prediction From a Single Image
By allowing for interactions between the depth and semantic information, the joint network provides more accurate depth prediction than a state-of-the-art CNN trained solely for depth prediction [5].
Attention to Scale: Scale-aware Semantic Image Segmentation
We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model.
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems.
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
To tackle these difficulties, we propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which adapts to the local scales of objects and parts.
Ranked #8 on
Human Part Segmentation
on PASCAL-Part
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization
In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.
PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset
In this paper, we address the boundary detection task motivated by the ambiguities in current definition of edge detection.
Scene-Domain Active Part Models for Object Representation
In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images.
One Shot Learning via Compositions of Meaningful Patches
The task of discriminating one object from another is almost trivial for a human being.
Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
Mining 3D Key-Pose-Motifs for Action Recognition
Recognizing an action from a sequence of 3D skeletal poses is a challenging task.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.
Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e. g., multiple different cars.
Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples
This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e. g., different lighting conditions, different glasses).
Object Recognition with and without Objects
While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image.
SURGE: Surface Regularized Geometry Estimation from a Single Image
This paper introduces an approach to regularize 2. 5D surface normal and depth predictions at each pixel given a single input image.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image.
Regularizing Face Verification Nets For Pain Intensity Regression
In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification.
Multi-Context Attention for Human Pose Estimation
We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.
Ranked #8 on
Pose Estimation
on Leeds Sports Poses
NormFace: L2 Hypersphere Embedding for Face Verification
We show that both strategies, and small variants, consistently improve performance by between 0. 2% to 0. 4% on the LFW dataset based on two models.
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation.
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.
Ranked #1 on
Pancreas Segmentation
on TCIA Pancreas-CT Dataset
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.
Adversarial Attacks Beyond the Image Space
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images.
Single-Shot Object Detection with Enriched Semantics
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks.
Ranked #88 on
Semantic Segmentation
on NYU Depth v2
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.
Joint Shape Representation and Classification for Detecting PDAC
Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting.
Deep Nets: What have they ever done for Vision?
We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves.
Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans.
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution
We introduce PreCo, a large-scale English dataset for coreference resolution.
Phase Collaborative Network for Two-Phase Medical Image Segmentation
However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient.
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.
Snapshot Distillation: Teacher-Student Optimization in One Generation
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world.
Elastic Boundary Projection for 3D Medical Image Segmentation
The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations.
Thickened 2D Networks for Efficient 3D Medical Image Segmentation
With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information.
Patch-based 3D Human Pose Refinement
State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run.
Adversarial Examples for Edge Detection: They Exist, and They Transfer
Convolutional neural networks have recently advanced the state of the art in many tasks including edge and object boundary detection.
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.
Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints
Scene context is a powerful constraint on the geometry of objects within the scene in cases, such as surveillance, where the camera geometry is unknown and image quality may be poor.
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.
Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks
3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.
Organ size increases with obesity and correlates with cancer risk
Obesity increases significantly cancer risk in various organs.
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation.
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
We proposed a pair of cross-view transformers to transform the feature maps into the other view and introduce cross-view consistency loss on them.
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
Ranked #4 on
Medical Image Segmentation
on ACDC
Multi-phase Deformable Registration for Time-dependent Abdominal Organ Variations
Human body is a complex dynamic system composed of various sub-dynamic parts.
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients.
External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma.
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
This gives rise to significantly more robust segmentation of image objects in complex scenes and under adverse conditions.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
