4 citations · 10 across the 18 of their papers we have counts for
19 papers
Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking
Xiaozhen Qiao, Da Zhang, Yubin Guo +3
UAV Anti-UAV tracking is an emerging low-altitude security task for localizing an adversarial UAV using the onboard camera of a moving observer UAV. It differs from conventional UA…
An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
Bingyu Li, Da Zhang, Tao Huo +3
Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains…
Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Bingyu Li, Tao Huo, Haocheng Dong +4
Open-vocabulary remote sensing image segmentation (OVRSIS) aims to segment text-specified categories beyond a fixed label space. Its key challenge is to maintain reliable pixel--te…
AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors
Xiaozhen Qiao, Wenjia Wang, Zhiyuan Zhao +4
Reconstructing 3D humans from images captured at multiple perspectives typically requires pre-calibration, like using checkerboards or MVS algorithms, which limits scalability and…
Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs
Siqu Ou, Tianrui Wan, Zhiyuan Zhao +2
While chain-of-thought (CoT) reasoning has substantially improved multimodal large language models (MLLMs) on complex reasoning tasks, existing approaches largely rely on long text…
FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis
Da Zhang, Bingyu Li, Zhiyuan Zhao +3
Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology, underpinning key tasks including classification, forecasting, and anomaly…
UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting
Da Zhang, Bingyu Li, Zhuyuan Zhao +3
As multimodal data proliferates across diverse real-world applications, leveraging heterogeneous information such as texts and timestamps for accurate time series forecasting (TSF)…
FAIM: Frequency-Aware Interactive Mamba for Time Series Classification
Da Zhang, Bingyu Li, Zhiyuan Zhao +4
Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition. TSC tasks require mod…
Exploring the Underwater World Segmentation without Extra Training
Bingyu Li, Tao Huo, Da Zhang +3
Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain largely limited to terrestrial sce…
UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding
Da Zhang, Chenggang Rong, Bingyu Li +4
Large vision-language models (VLMs) have achieved remarkable success in natural scene understanding, yet their application to underwater environments remains largely unexplored. Un…
MARIS: Marine Open-Vocabulary Instance Segmentation with Geometric Enhancement and Semantic Alignment
Bingyu Li, Feiyu Wang, Da Zhang +3
Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support ev…
SVGen: Interpretable Vector Graphics Generation with Large Language Models
Feiyu Wang, Zhiyuan Zhao, Yuandong Liu +4
Scalable Vector Graphics (SVG) is widely used in front-end development and UI/UX design due to its scalability, editability, and rendering efficiency. However, turning creative ide…
LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges
Haoyang Li, Huan Gao, Zhiyuan Zhao +3
The widespread adoption of Large Language Models (LLMs) has heightened concerns about their security, particularly their vulnerability to jailbreak attacks that leverage crafted pr…
WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code
Zhiyu Lin, Zhengda Zhou, Zhiyuan Zhao +4
With the rapid advancement of Generative AI technology, Multimodal Large Language Models(MLLMs) have the potential to act as AI software engineers capable of executing complex web…
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Muzhi Dai, Jiashuo Sun, Zhiyuan Zhao +4
Aligning large vision-language models (LVLMs) with human preferences is challenging due to the scarcity of fine-grained, high-quality, and multimodal preference data without human…
FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Open-vocabulary segmentation aims to identify and segment specific regions and objects based on text-based descriptions. A common solution is to leverage powerful vision-language m…
StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Multimodal semantic segmentation shows significant potential for enhancing segmentation accuracy in complex scenes. However, current methods often incorporate specialized feature f…
U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Multimodal semantic segmentation is a pivotal component of computer vision and typically surpasses unimodal methods by utilizing rich information set from various sources.Current m…
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Haojian Huang, Xiaozhen Qiao, Zhuo Chen +5
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated…