3 citations · 10 across the 16 of their papers we have counts for
7 papers · 1 filter
From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images
Yiming Chen, Junlin Han, Tianyi Bai +3
While Multimodal Large Language Models (MLLMs) are adept at answering what is in an image-identifying objects and describing scenes-they often lack the ability to understand how an…
Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training
Junlin Han, Shengbang Tong, David Fan +4
Large Language Models (LLMs), despite being trained on text alone, surprisingly develop rich visual priors. These priors allow latent visual capabilities to be unlocked for vision…
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…
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…
WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild
Morris Alper, David Novotny, Filippos Kokkinos +2
Despite recent advances in sparse novel view synthesis (NVS) applied to object-centric scenes, scene-level NVS remains a challenge. A central issue is the lack of available clean m…
Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects
Shalini Maiti, Lourdes Agapito, Filippos Kokkinos
Rapid advancements in text-to-3D generation require robust and scalable evaluation metrics that align closely with human judgment, a need unmet by current metrics such as PSNR and…
VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models
Yufan Ren, Konstantinos Tertikas, Shalini Maiti +4
Large Vision-Language Models (LVLMs) struggle with puzzles, which require precise perception, rule comprehension, and logical reasoning. Assessing and enhancing their performance i…