3 papers
cs.CL2026
On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training
Xueqing Wu, Yu-Chi Lin, Kai-Wei Chang +1
Post-training has greatly improved reasoning in frontier vision-language models, yet its gains for perception remain comparatively limited, creating a bottleneck for end-to-end vis…
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…