3 papers
cs.CV2026
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…
cs.CV2025
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
Junzhang Liu, Zhecan Wang, Hammad Ayyubi +5
Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive…
cs.CV2025
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…