4 papers
ENTER: Event Based Interpretable Reasoning for VideoQA
Hammad Ayyubi, Junzhang Liu, Ali Asgarov +10
In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where…
Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models
Md. Atabuzzaman, Andrew Zhang, Chris Thomas
Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classifi…
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models
Md. Atabuzzaman, Ali Asgarov, Chris Thomas
Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal…
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images
Zhecan Wang, Junzhang Liu, Chia-Wei Tang +11
Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perfor…