81 citations · 172 across the 23 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…