81 citations · 172 across the 23 of their papers we have counts for
24 papers
SJD-VP: Speculative Jacobi Decoding with Verification Prediction for Autoregressive Image Generation
Bingqi Shan, Baoquan Zhang, Xiaochen Qi +3
Speculative Jacobi Decoding (SJD) has emerged as a promising method for accelerating autoregressive image generation. Despite its potential, existing SJD approaches often suffer fr…
LogiDroid: Individual Functional Test Generation via Business Logic Extraction and Adaptation
Yakun Zhang, Zihan Wang, Xinzhi Peng +6
Functional testing is essential for verifying that the business logic of mobile applications aligns with user requirements. Despite its importance, functional testing remains heavi…
Prototype Optimization with Neural ODE for Few-Shot Learning
Baoquan Zhang, Shanshan Feng, Bingqi Shan +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a…
LG-VQ: Language-Guided Codebook Learning
Guotao Liang, Baoquan Zhang, Yaowei Wang +6
Vector quantization (VQ) is a key technique in high-resolution and high-fidelity image synthesis, which aims to learn a codebook to encode an image with a sequence of discrete code…
MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Information
Jiajun Liang, Baoquan Zhang, Yunming Ye +3
The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather…
Four-hour thunderstorm nowcasting using a deep diffusion model of satellite data
Kuai Dai, Xutao Li, Junying Fang +6
Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to infrastructu…
Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling
Baoquan Zhang, Huaibin Wang, Luo Chuyao +5
Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effec…
DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting
Demin Yu, Xutao Li, Yunming Ye +5
Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations, which can serve both meteorological sc…
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
Huiwei Lin, Shanshan Feng, Baoquan Zhang +3
Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-ba…
HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning
Huiwei Lin, Shanshan Feng, Baoquan Zhang +2
Online continual learning, aimed at developing a neural network that continuously learns new data from a single pass over an online data stream, generally suffers from catastrophic…
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Baoquan Zhang, Chuyao Luo, Demin Yu +4
Equipping a deep model the abaility of few-shot learning, i.e., learning quickly from only few examples, is a core challenge for artificial intelligence. Gradient-based meta-learni…
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
Huiwei Lin, Baoquan Zhang, Shanshan Feng +2
Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are…
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Baoquan Zhang, Hao Jiang, Xutao Li +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-le…
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
Baoquan Zhang, Shanshan Feng, Xutao Li +2
Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to addr…
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
Chuyao Luo, ZhengZhang, Rui Ye +2
Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasin…
Prototype Completion for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye +1
Few-shot learning aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tun…
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Baoquan Zhang, Xutao Li, Shanshan Feng +2
Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-train…
Prototype Completion with Primitive Knowledge for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye +2
Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the probl…
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
Yuan Yao, Xutao Li, Yu Zhang +1
Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing H…
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
Baoquan Zhang, Ka-Cheong Leung, Yunming Ye +1
Meta-learning has been proved to be an effective framework to address few-shot learning problems. The key challenge is how to minimize the generalization error of base learner acro…
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Feng Liu, Weiwen Liu, Xutao Li +1
Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling th…
Heterogeneous Domain Adaptation via Soft Transfer Network
Yuan Yao, Yu Zhang, Xutao Li +1
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a…
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Feng Liu, Ruiming Tang, Xutao Li +5
Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regressio…
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Feng Liu, Ruiming Tang, Xutao Li +3
Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delive…