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cs.CV2026
TwinICL: Diagnosing Multimodal In-Context Learning through Paired Counterfactuals
Zihan Xue, Po-Yi Lu, Serhii Honcharenko +5
In-context learning (ICL) enables models to infer tasks from demonstrations, but existing benchmarks generally lack matched text and image versions needed to compare ICL performanc…
cs.CV2024
VISCO: Benchmarking Fine-Grained Critique and Correction Towards Self-Improvement in Visual Reasoning
Xueqing Wu, Yuheng Ding, Bingxuan Li +4
The ability of large vision-language models (LVLMs) to critique and correct their reasoning is an essential building block towards their self-improvement. However, a systematic ana…
cs.CV2024
Verbalized Representation Learning for Interpretable Few-Shot Generalization
Cheng-Fu Yang, Da Yin, Wenbo Hu +4
Humans recognize objects after observing only a few examples, a remarkable capability enabled by their inherent language understanding of the real-world environment. Developing ver…