140 citations · 820 across the 54 of their papers we have counts for
59 papers
-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling
Thanh-Dat Truong, Sarah Alharbi, Susan Gauch +3
Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. However, current graph modeling me…
Knowledge Consultation for Semi-Supervised Semantic Segmentation
Thuan Than, Nhat-Anh Nguyen-Dang, Dung Nguyen +7
Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the succ…
Efficient Autoregressive Audio Modeling via Next-Scale Prediction
Kai Qiu, Xiang Li, Hao Chen +5
Audio generation has achieved remarkable progress with the advance of sophisticated generative models, such as diffusion models (DMs) and autoregressive (AR) models. However, due t…
A Reference-Based 3D Semantic-Aware Framework for Accurate Local Facial Attribute Editing
Yu-Kai Huang, Yutong Zheng, Yen-Shuo Su +4
Facial attribute editing plays a crucial role in synthesizing realistic faces with specific characteristics while maintaining realistic appearances. Despite advancements, challenge…
RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection
Fangyi Chen, Han Zhang, Zhantao Yang +3
Open-vocabulary object detection (OVD) requires solid modeling of the region-semantic relationship, which could be learned from massive region-text pairs. However, such data is lim…
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Xuan-Bac Nguyen, Chi Nhan Duong, Marios Savvides +3
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale…
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
Hao Chen, Ran Tao, Yue Fan +6
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization pe…
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
Fangyi Chen, Han Zhang, Kai Hu +3
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the tra…
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
Hao Chen, Yue Fan, Yidong Wang +5
Semi-supervised learning (SSL) has shown great promise in leveraging unlabeled data to improve model performance. While standard SSL assumes uniform data distribution, we consider…
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
Thanh-Dat Truong, Chi Nhan Duong, Ngan Le +2
In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challe…
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Hao Chen, Ran Tao, Han Zhang +6
While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-s…
USB: A Unified Semi-supervised Learning Benchmark for Classification
Yidong Wang, Hao Chen, Yue Fan +19
Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation pro…
Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling
Ran Tao, Han Zhang, Yutong Zheng +1
In recent works, utilizing a deep network trained on meta-training set serves as a strong baseline in few-shot learning. In this paper, we move forward to refine novel-class featur…
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Yidong Wang, Hao Chen, Qiang Heng +9
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.…
Unitail: Detecting, Reading, and Matching in Retail Scene
Fangyi Chen, Han Zhang, Zaiwang Li +7
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we intro…
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey
Ngan Le, Vidhiwar Singh Rathour, Kashu Yamazaki +2
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remar…
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
Zhiqiang Shen, Zechun Liu, Dejia Xu +3
This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the motivation beh…
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
Yutong Zheng, Yu-Kai Huang, Ran Tao +2
We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation l…
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
Chenchen Zhu, Fangyi Chen, Uzair Ahmed +2
Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data s…
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
Zhiqiang Shen, Zechun Liu, Jie Qin +2
The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model…
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
Zhiqiang Shen, Zechun Liu, Jie Qin +3
Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural netw…
Offset Curves Loss for Imbalanced Problem in Medical Segmentation
Ngan Le, Trung Le, Kashu Yamazaki +3
Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high p…
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
Ngan Le, Kashu Yamazaki, Dat Truong +2
In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segment…
Online Ensemble Model Compression using Knowledge Distillation
Devesh Walawalkar, Zhiqiang Shen, Marios Savvides
This paper presents a novel knowledge distillation based model compression framework consisting of a student ensemble. It enables distillation of simultaneously learnt ensemble kno…