research
∙
08/19/2023
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Generalizing deep learning models to unknown target domain distribution ...
share
research
∙
03/31/2023
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
Domain adaptation helps generalizing object detection models to target d...
research
∙
03/20/2023
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Deploying models on target domain data subject to distribution shift req...
research
∙
03/13/2023
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Object detection in remote sensing images relies on a large amount of la...
research
∙
12/14/2022
Deep Negative Correlation Classification
Ensemble learning serves as a straightforward way to improve the perform...
research
∙
06/06/2022
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
Deploying models on target domain data subject to distribution shift req...
research
∙
05/18/2022
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
Recent work on curvilinear structure segmentation has mostly focused on ...
research
∙
05/16/2022
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
3D object detection has recently received much attention due to its grea...
research
∙
05/06/2022
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
Addressing the annotation challenge in 3D Point Cloud segmentation has i...
research
∙
05/06/2022
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Semi-supervised learning (SSL) addresses the lack of labeled data by exp...
research
∙
05/02/2022
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
Semantic understanding of 3D point cloud relies on learning models with ...
research
∙
04/06/2022
SPIDER-WEB enables stable, repairable, and encryptible algorithms under arbitrary local biochemical constraints in DNA-based storage
DNA has been considered as a promising medium for storing digital inform...
research
∙
12/11/2021
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and im...
research
∙
03/30/2021
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
The ability to understand the ways to interact with objects from visual ...
research
∙
03/09/2021
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich ...
research
∙
01/18/2021
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Semantic segmentation of 3D point clouds relies on training deep models ...
research
∙
12/14/2020
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
This paper is motivated from a fundamental curiosity on what defines a c...
research
∙
04/08/2020
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
Point cloud analysis has received much attention recently; and segmentat...
research
∙
08/16/2019
Zero-Shot Crowd Behavior Recognition
Understanding crowd behavior in video is challenging for computer vision...
research
∙
08/16/2019
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models
Many real-world video sequences cannot be conveniently categorized as ge...
research
∙
04/10/2019
C3AE: Exploring the Limits of Compact Model for Age Estimation
Age estimation is a classic learning problem in computer vision. Many la...
research
∙
04/03/2019
Learning for Multi-Type Subspace Clustering
Subspace clustering has been extensively studied from the hypothesis-and...
research
∙
03/06/2019
Robust Video Background Identification by Dominant Rigid Motion Estimation
The ability to identify the static background in videos captured by a mo...
research
∙
01/29/2019
Learning for Multi-Model and Multi-Type Fitting
Multi-model fitting has been extensively studied from the random samplin...
research
∙
05/08/2018
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Image ordinal classification refers to predicting a discrete target valu...
research
∙
04/06/2018
Motion Segmentation by Exploiting Complementary Geometric Models
Many real-world sequences cannot be conveniently categorized as general ...
research
∙
02/01/2018
Perceptual Compressive Sensing
This paper proposes perceptual compressive sensing. The network is compo...
research
∙
11/21/2017
Fully-Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Recently, deep learning methods have made a significant improvement in c...
research
∙
11/26/2016
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassi...
research
∙
04/15/2016
Latent Model Ensemble with Auto-localization
Deep Convolutional Neural Networks (CNN) have exhibited superior perform...
research
∙
11/13/2015
Transductive Zero-Shot Action Recognition by Word-Vector Embedding
The number of categories for action recognition is growing rapidly and i...
research
∙
07/27/2015
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization
The growing rate of public space CCTV installations has generated a need...
research
∙
02/05/2015