3 citations · 3 across the 3 of their papers we have counts for
6 papers
Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation
Yangneng Chen, Jing Li
Large Vision-Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination, where outputs are fluent yet inconsistent with…
VIABLE: A Visually Impaired Assistance Benchmark for VLM-as-a-Judge Evaluation
Yi Zhao, Siqi Wang, Zhe Hu +2
AI-based Visually Impaired Assistance (VIA) remains challenging, largely due to the high cost of human evaluation. The VLM-as-a-Judge paradigm may offer a promising alternative, al…
Sighted by Default: Addressing Implicit Vision Assumptions in Real-Time VLM Assistance for BLV Users
Yi Zhao, Siqi Wang, Qiqun Geng +2
Vision-Language Model (VLM)-based assistance is reshaping independence for blind and low-vision (BLV) users, yet current tools fail in dynamic settings. While request-response arch…
LaF-GRPO: In-Situ Navigation Instruction Generation for the Visually Impaired via GRPO with LLM-as-Follower Reward
Yi Zhao, Siqi Wang, Jing Li
Navigation instruction generation for visually impaired (VI) individuals (NIG-VI) is critical yet relatively underexplored. This study focuses on generating precise, in-situ, step-…
CAP: Data Contamination Detection via Consistency Amplification
Yi Zhao, Jing Li, Linyi Yang
Large language models (LLMs) are widely used, but concerns about data contamination challenge the reliability of LLM evaluations. Existing contamination detection methods are often…
VIALM: A Survey and Benchmark of Visually Impaired Assistance with Large Models
Yi Zhao, Yilin Zhang, Rong Xiang +2
Visually Impaired Assistance (VIA) aims to automatically help the visually impaired (VI) handle daily activities. The advancement of VIA primarily depends on developments in Comput…