2 citations · 3 across the 16 of their papers we have counts for
16 papers
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
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…
Beyond Prompt Degradation: Prototype-guided Dual-pool Prompting for Incremental Object Detection
Yaoteng Zhang, Zhou Qing, Junyu Gao +1
Incremental Object Detection (IOD) aims to continuously learn new object categories without forgetting previously learned ones. Recently, prompt-based methods have gained popularit…
UNICBench: UNIfied Counting Benchmark for MLLM
Chenggang Rong, Tao Han, Zhiyuan Zhao +5
Counting is a core capability for multimodal large language models (MLLMs), yet there is no unified counting dataset to rigorously evaluate this ability across image, text, and aud…
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…
SignEye: Traffic Sign Interpretation from Vehicle First-Person View
Chuang Yang, Xu Han, Tao Han +5
Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navi…
Real-Time Text Detection with Similar Mask in Traffic, Industrial, and Natural Scenes
Xu Han, Junyu Gao, Chuang Yang +2
Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. U…
Focus Entirety and Perceive Environment for Arbitrary-Shaped Text Detection
Xu Han, Junyu Gao, Chuang Yang +2
Due to the diversity of scene text in aspects such as font, color, shape, and size, accurately and efficiently detecting text is still a formidable challenge. Among the various det…
Spotlight Text Detector: Spotlight on Candidate Regions Like a Camera
Xu Han, Junyu Gao, Chuang Yang +2
The irregular contour representation is one of the tough challenges in scene text detection. Although segmentation-based methods have achieved significant progress with the help of…
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…