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Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection
Juil Koo, Daehyeon Choi, Sangwoo Youn +2
Vision Language Models (VLMs) excel at visual question answering (VQA) but remain limited to snapshot vision, reasoning from static images. In contrast, embodied agents require amb…
DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
Mingue Park, Prin Phunyaphibarn, Phillip Y. Lee +1
We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiring retraining, fine-tuning, or s…
Perspective-Aware Reasoning in Vision-Language Models via Mental Imagery Simulation
Phillip Y. Lee, Jihyeon Je, Chanho Park +3
We present a framework for perspective-aware reasoning in vision-language models (VLMs) through mental imagery simulation. Perspective-taking, the ability to perceive an environmen…
Unconditional Priors Matter! Improving Conditional Generation of Fine-Tuned Diffusion Models
Prin Phunyaphibarn, Phillip Y. Lee, Jaihoon Kim +1
Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to use a single network to learn b…