Search Results for author:
Found 44 papers, 9 papers with code
Mitigating Biased Activation in Weakly-supervised Object Localization via Counterfactual Learning
In this paper, we focus on an under-explored issue of biased activation in prior weakly-supervised object localization methods based on Class Activation Mapping (CAM).
Text-guided Eyeglasses Manipulation with Spatial Constraints
To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively.
UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation
Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved translations, which is omitted in previous works.
Adaptive Stylization Modulation for Domain Generalized Semantic Segmentation
Therefore, we address the problem of domain generalization for semantic segmentation tasks to reduce the need to acquire and label additional data.
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities
We show that the resolution of multi-illumination imaging is approximately determined by the new imaging kernel from our operator theory and better resolution can be realized by sparsity-promoting techniques in practice but only for resolving very sparse sources.
Super-resolution of positive near-colliding point sources
In this paper, we analyze the capacity of super-resolution of one-dimensional positive sources.
A mathematical theory of super-resolution and diffraction limit
The first contribution in this paper is two location-amplitude identities characterizing the relations between source locations and amplitudes in the super-resolution problem.
A mathematical theory of resolution limits for super-resolution of positive sources
The superresolving capacity for number and location recoveries in the super-resolution of positive sources is analyzed in this work.
Meta Knowledge Condensation for Federated Learning
In this work, we first introduce a meta knowledge representation method that extracts meta knowledge from distributed clients.
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array
The stability result exhibits the optimal performance of sparsity promoting in solving such problems; (iii) Our techniques pave the way for improving the estimate for resolution limits in higher-dimensional super-resolutions to nearly optimal; (iv) Inspired by these new techniques, we propose a new coordinate-combination-based model order detection algorithm for two-dimensional DOA estimation and theoretically demonstrate its optimal performance, and (v) we also propose a new coordinate-combination-based MUSIC algorithm for super-resolving sources in two-dimensional DOA estimation.
Dynamic super-resolution in particle tracking problems
This result is derived by an observation that the inherent cut-off frequency for the velocity recovery can be viewed as the total observation time multiplies the cut-off frequency of the imaging system, which may lead to a better resolution limit as compared to the one for each diffraction-limited frame.
A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain.
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning
To deploy SSDR-AL in a more practical scenario, we design a noise-aware iterative labeling strategy to confront the "noisy annotation" problem introduced by the previous "dominant labeling" strategy in superpoints.
Mathematical foundation of sparsity-based multi-illumination super-resolution
By this estimation, we reveal the dependence of the resolution on the cut-off frequency of the imaging system, the SNR, the sparsity of point scatterers, and the incoherence of illumination patterns.
Onsite Non-Line-of-Sight Imaging via Online Calibrations
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification
Person Re-Identification
SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on
Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose.
Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails
Specifically, we design a novel cross-modal transformer module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.
Mathematical Theory of Computational Resolution Limit in Multi-dimensions
Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems.
Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
Non-line-of-Sight Imaging via Neural Transient Fields
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
Federated Generative Adversarial Learning
This work studies training generative adversarial networks under the federated learning setting.
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
The first one is that our dataset is not fully labeled, i. e., only a subset of all lesion instances are marked.
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution
Our methods can significantly reduce the cost of development and maintenance of anomaly detection.
Face Hallucination with Finishing Touches
The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i. e., fine-grained facial components, to attain a frontal HR face image with authentic details.
Very Long Natural Scenery Image Prediction by Outpainting
The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input.
Unsupervised Eyeglasses Removal in the Wild
Given two facial images with and without eyeglasses, the proposed model learns to swap the eye area in two faces.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers
Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e. g., image recognition) and natural language processing (e. g., text classification).
Filter Pruning by Switching to Neighboring CNNs with Good Attributes
In addition, when evaluating the filter importance, only the magnitude information of the filters is considered.
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex.
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.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning.
Ranked #4 on
Node Classification
on Cora: fixed 20 node per class
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs.
Facial Expression Recognition via a Boosted Deep Belief Network
A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction.
