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cs.CV2025★ 1 cited
Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples
Weiwei Li, Junzhuo Liu, Yuanyuan Ren +3
Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods ty…
cs.LG2025
GeoRef: Referring Expressions in Geometry via Task Formulation, Synthetic Supervision, and Reinforced MLLM-based Solutions
Bing Liu, Wenqiang Yv, Xuzheng Yang +6
AI-driven geometric problem solving is a complex vision-language task that requires accurate diagram interpretation, mathematical reasoning, and robust cross-modal grounding. A fou…
cs.CV2025
New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration
Xuzheng Yang, Junzhuo Liu, Peng Wang +3
Referring Expression Comprehension (REC) is a foundational cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image groun…