3 citations · 3 across the 3 of their papers we have counts for
4 papers
Same Answer, Different Representations: Hidden instability in VLMs
Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena +6
The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions reflect stable multimodal processi…
From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluation
Seokhee Hong, Sunkyoung Kim, Guijin Son +3
The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applica…
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
Zhaowei Wang, Wenhao Yu, Xiyu Ren +9
The rapid extension of context windows in large vision-language models has given rise to long-context vision-language models (LCVLMs), which are capable of handling hundreds of ima…
Data-Driven Compression of Convolutional Neural Networks
Ramit Pahwa, Manoj Ghuhan Arivazhagan, Ankur Garg +3
Deploying trained convolutional neural networks (CNNs) to mobile devices is a challenging task because of the simultaneous requirements of the deployed model to be fast, lightweigh…