14 papers
Adaptive Multi-Scale Channel-Spatial Attention Aggregation Framework for 3D Indoor Semantic Scene Completion Toward Assisting Visually Impaired
Qi He, XiangXiang Wang, Jingtao Zhang +5
Independent indoor mobility remains a critical challenge for individuals with visual impairments, largely due to the limited capability of existing assistive systems in detecting f…
RPIQ: Residual-Projected Multi-Collaboration Closed-Loop and Single Instance Quantization for Visually Impaired Assistance
Xuanyu Wang, Haisen Su, Jingtao Zhang +9
Visually impaired users face significant challenges in daily information access and real-time environmental perception, and there is an urgent need for intelligent assistive system…
Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges
Cheng Huang, Nyima Tashi, Fan Gao +19
Tibetan, one of the major low-resource languages in Asia, presents unique linguistic and sociocultural characteristics that pose both challenges and opportunities for AI research.…
BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images
Cheng Huang, Weizheng Xie, Fan Gao +8
Structural changes in retinal blood vessels are critical biomarkers for the onset and progression of glaucoma and other ocular diseases. However, current vessel segmentation approa…
Listening, Imagining & Refining: A Heuristic Optimized ASR Correction Framework with LLMs
Yutong Liu, Ziyue Zhang, Cheng Huang +4
Automatic Speech Recognition (ASR) systems remain prone to errors that affect downstream applications. In this paper, we propose LIR-ASR, a heuristic optimized iterative correction…
TMD-TTS: A Unified Tibetan Multi-Dialect Text-to-Speech Framework for Ü-Tsang, Amdo and Kham Speech Dataset Generation
Yutong Liu, Ziyue Zhang, Ban Ma-bao +7
Tibetan is a low-resource language with limited parallel speech corpora spanning its three major dialects (Ü-Tsang, Amdo, and Kham), limiting progress in speech modeling. To addres…
Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance
Xiangxiang Wang, Xuanyu Wang, YiJia Luo +4
Visually impaired individuals face significant challenges in environmental perception. Traditional assistive technologies often lack adaptive intelligence, focusing on individual c…
TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models
Fan Gao, Cheng Huang, Nyima Tashi +11
To address the severe data scarcity in Tibetan, a low-resource language spoken by over six million people, we introduce TIBSTC-CoT, the large-scale, multi-domain Tibetan dataset au…
Modeling Insider Filing Delays in Financial Markets with an Interpretable XGBoost Framework
Cheng Huang, Yao Ma, Fan Gao +10
Timely disclosure of insider transactions is a cornerstone of market transparency, yet delays in filing remain widespread and challenging to monitor at scale. This study introduces…
RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models
Jin Zhang, Fan Gao, Linyu Li +4
The rise of large language models has led to significant performance breakthroughs in named entity recognition (NER) for high-resource languages, yet there remains substantial room…
TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation
Yutong Liu, Feng Xiao, Ziyue Zhang +10
Multi-level Tibetan spelling correction addresses errors at both the character and syllable levels within a unified model. Existing methods focus mainly on single-level correction…
FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation
Yutong Liu, Ziyue Zhang, Ban Ma-bao +7
Tibetan is a low-resource language with minimal parallel speech corpora spanning its three major dialects-Ü-Tsang, Amdo, and Kham-limiting progress in speech modeling. To address t…
TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling
Cheng Huang, Fan Gao, Nyima Tashi +5
Large Language Models (LLMs) have achieved remarkable success in high-resource languages, yet progress in Tibetan remains severely constrained. While recent efforts have begun to a…
TLUE: A Tibetan Language Understanding Evaluation Benchmark
Fan Gao, Cheng Huang, Nyima Tashi +9
Large language models have made tremendous progress in recent years, but low-resource languages, like Tibetan, remain significantly underrepresented in their evaluation. Despite Ti…