11 citations · 24 across the 8 of their papers we have counts for
9 papers
Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Unsupervised cross-modal hashing enables efficient retrieval of semantically related instances across different modalities without requiring manual semantic annotation. However, ex…
Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existi…
FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
Rongzihan Song, Zhenyu Weng, Huiping Zhuang +3
Multiple object tracking (MOT) involves identifying multiple targets and assigning them corresponding IDs within a video sequence, where occlusions are often encountered. Recent me…
Constant Sequence Extension for Fast Search Using Weighted Hamming Distance
Zhenyu Weng, Huiping Zhuang, Haizhou Li +1
Representing visual data using compact binary codes is attracting increasing attention as binary codes are used as direct indices into hash table(s) for fast non-exhaustive search.…
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Hongxin Wei, Huiping Zhuang, Renchunzi Xie +4
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been s…
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Huiping Zhuang, Zhenyu Weng, Hongxin Wei +3
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss…
Analytic Learning of Convolutional Neural Network For Pattern Recognition
Huiping Zhuang, Zhiping Lin, Yimin Yang +1
Training convolutional neural networks (CNNs) with back-propagation (BP) is time-consuming and resource-intensive particularly in view of the need to visit the dataset multiple tim…
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Huiping Zhuang, Zhiping Lin, Kar-Ann Toh
Decoupled learning is a branch of model parallelism which parallelizes the training of a network by splitting it depth-wise into multiple modules. Techniques from decoupled learnin…
Fully Decoupled Neural Network Learning Using Delayed Gradients
Huiping Zhuang, Yi Wang, Qinglai Liu +2
Training neural networks with back-propagation (BP) requires a sequential passing of activations and gradients, which forces the network modules to work in a synchronous fashion. T…