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Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical information resides in small regions or detailed, cluttered scenes. While several approaches address this limitation, a systematic understanding of why models fail at high re...
This work introduces a human-aligned evaluation framework for text-to-SVG generation, and develops two complementary evaluators: CLIP scorers adapted to vector graphics and then aligned to human preferences, for fast large-scale evaluation and a VLM judge trained with supervised fine-tuning and reward-shaped reinforcem...
This work proposes a novel test-time alignment approach that leverages trajectory-guided structured sampling for dynamic inference-time refinement, achieving better alignment with visual grounding and ensuring logical consistency, and demonstrates that this approach significantly improves accuracy without incurring pro...
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