research
∙
08/18/2023
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition
We tackle the data scarcity challenge in few-shot point cloud recognitio...
share
research
∙
03/25/2023
Freestyle Layout-to-Image Synthesis
Typical layout-to-image synthesis (LIS) models generate images for a clo...
research
∙
03/24/2023
Class-Incremental Exemplar Compression for Class-Incremental Learning
Exemplar-based class-incremental learning (CIL) finetunes the model with...
research
∙
03/22/2023
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because...
research
∙
03/18/2023
Extracting Class Activation Maps from Non-Discriminative Features as well
Extracting class activation maps (CAM) from a classification model often...
research
∙
03/17/2023
Semantic Scene Completion with Cleaner Self
Semantic Scene Completion (SSC) transforms an image of single-view depth...
research
∙
01/14/2023
RMM: Reinforced Memory Management for Class-Incremental Learning
Class-Incremental Learning (CIL) [40] trains classifiers under a strict ...
research
∙
01/11/2023
Online Hyperparameter Optimization for Class-Incremental Learning
Class-incremental learning (CIL) aims to train a classification model wh...
research
∙
11/20/2022
Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation
Extracting class activation maps (CAM) is a key step for weakly-supervis...
research
∙
10/16/2022
Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames
Cross-modal video retrieval aims to retrieve the semantically relevant v...
research
∙
08/06/2022
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
Out-Of-Distribution generalization (OOD) is all about learning invarianc...
research
∙
07/25/2022
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
We are interested in learning robust models from insufficient data, with...
research
∙
07/25/2022
On Mitigating Hard Clusters for Face Clustering
Face clustering is a promising way to scale up face recognition systems ...
research
∙
03/02/2022
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
Extracting class activation maps (CAM) is arguably the most standard ste...
research
∙
12/31/2021
Deconfounded Visual Grounding
We focus on the confounding bias between language and location in the vi...
research
∙
10/28/2021
Self-Supervised Learning Disentangled Group Representation as Feature
A good visual representation is an inference map from observations (imag...
research
∙
08/19/2021
Causal Attention for Unbiased Visual Recognition
Attention module does not always help deep models learn causal features ...
research
∙
07/23/2021
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
Existing Unsupervised Domain Adaptation (UDA) literature adopts the cova...
research
∙
05/12/2021
A Large-Scale Benchmark for Food Image Segmentation
Food image segmentation is a critical and indispensible task for develop...
research
∙
03/01/2021
Counterfactual Zero-Shot and Open-Set Visual Recognition
We present a novel counterfactual framework for both Zero-Shot Learning ...
research
∙
10/12/2020
Counterfactual Variable Control for Robust and Interpretable Question Answering
Deep neural network based question answering (QA) models are neither rob...
research
∙
10/10/2020
Meta-Aggregating Networks for Class-Incremental Learning
Class-Incremental Learning (CIL) aims to learn a classification model wi...
research
∙
09/28/2020
Interventional Few-Shot Learning
We uncover an ever-overlooked deficiency in the prevailing Few-Shot Lear...
research
∙
09/26/2020
Causal Intervention for Weakly-Supervised Semantic Segmentation
We present a causal inference framework to improve Weakly-Supervised Sem...
research
∙
07/18/2020
Feature Pyramid Transformer
Feature interactions across space and scales underpin modern visual reco...
research
∙
02/27/2020
Visual Commonsense R-CNN
We present a novel unsupervised feature representation learning method, ...
research
∙
02/24/2020
Mnemonics Training: Multi-Class Incremental Learning without Forgetting
Multi-Class Incremental Learning (MCIL) aims to learn new concepts by in...
research
∙
10/07/2019
Meta-Transfer Learning through Hard Tasks
Meta-learning has been proposed as a framework to address the challengin...
research
∙
06/03/2019
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Few-shot classification (FSC) is challenging due to the scarcity of labe...
research
∙
04/18/2019
A Novel BiLevel Paradigm for Image-to-Image Translation
Image-to-image (I2I) translation is a pixel-level mapping that requires ...
research
∙
04/17/2019
LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning
Meta-learning has been shown to be an effective strategy for few-shot le...
research
∙
12/24/2018
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
We propose a novel approach to jointly perform 3D object retrieval and p...
research
∙
12/06/2018
Meta-Transfer Learning for Few-Shot Learning
Meta-learning has been proposed as a framework to address the challengin...
research
∙
04/13/2018
A Hybrid Model for Identity Obfuscation by Face Replacement
As more and more personal photos are shared and tagged in social media, ...
research
∙
12/07/2017
Disentangled Person Image Generation
Generating novel, yet realistic, images of persons is a challenging task...
research
∙
11/24/2017
Natural and Effective Obfuscation by Head Inpainting
As more and more personal photos are shared online, being able to obfusc...
research
∙
05/25/2017
