2 citations · 3 across the 9 of their papers we have counts for
9 papers
Batch Loss Score for Dynamic Data Pruning
Qing Zhou, Bingxuan Zhao, Tao Yang +3
Dynamic data pruning accelerates deep learning by selectively omitting less informative samples during training. While per-sample loss is a common importance metric, obtaining it c…
Efficient Reasoning via Thought Compression for Language Segmentation
Qing Zhou, Shiyu Zhang, Yuyu Jia +4
Chain-of-thought (CoT) reasoning has significantly improved the performance of large multimodal models in language-guided segmentation, yet its prohibitive computational cost, stem…
One-Shot Crowd Counting With Density Guidance For Scene Adaptation
Jiwei Chen, Qi Wang, Junyu Gao +3
Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance scenes. To improve the generaliz…
Scale Efficient Training for Large Datasets
Qing Zhou, Junyu Gao, Qi Wang
The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly ineffic…
A Benchmark for Multi-Lingual Vision-Language Learning in Remote Sensing Image Captioning
Qing Zhou, Tao Yang, Junyu Gao +3
Remote Sensing Image Captioning (RSIC) is a cross-modal field bridging vision and language, aimed at automatically generating natural language descriptions of features and scenes i…
A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot
Haoxuan Ding, Qi Wang, Junyu Gao +1
Traditional license plate detection and recognition models are often trained on closed datasets, limiting their ability to handle the diverse license plate formats across different…
Text-only Synthesis for Image Captioning
Qing Zhou, Junlin Huang, Qiang Li +2
From paired image-text training to text-only training for image captioning, the pursuit of relaxing the requirements for high-cost and large-scale annotation of good quality data r…
Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation
Yuyu Jia, Wei Huang, Junyu Gao +2
Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous…
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text
Yuyu Jia, Qing Zhou, Wei Huang +2
Few-shot learning aims to generalize the recognizer from seen categories to an entirely novel scenario. With only a few support samples, several advanced methods initially introduc…