6 citations · 12 across the 7 of their papers we have counts for
7 papers
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
Chengyuan Zhang, Yilin Zhang, Lei Zhu +6
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is…
LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers
Feng Xue, Yu Li, Deyin Liu +3
Lipreading refers to understanding and further translating the speech of a speaker in the video into natural language. State-of-the-art lipreading methods excel in interpreting ove…
Asymmetric Cross-Scale Alignment for Text-Based Person Search
Zhong Ji, Junhua Hu, Deyin Liu +2
Text-based person search (TBPS) is of significant importance in intelligent surveillance, which aims to retrieve pedestrian images with high semantic relevance to a given text desc…
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
Lin Wu, Deyin Liu, Wenying Zhang +5
Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for…
Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense
Deyin Liu, Lin Wu, Haifeng Zhao +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce…
HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
Deyin Liu, Xu Chen, Zhi Zhou +1
Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI applications. Conventional approaches to training DNNs are generally implemented at central se…
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
Deyin Liu, Lin Wu, Xue Li
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient…