6 papers · 1 filter
Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models
Kiet T. Nguyen, Hanbo Shim, Jinwoo Kim +1
Vision-language models (VLMs) have achieved strong image and video understanding, yet their visual-spatial representations remain geometrically fragile, leading to failures in spat…
Training-Free Refinement of Flow Matching with Divergence-based Sampling
Yeonwoo Cha, Jaehoon Yoo, Semin Kim +3
Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecting each sample from a simple prior to…
When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models
Jiho Choi, Jaemin Kim, Sanghwan Kim +2
Attention sinks are defined as tokens that attract disproportionate attention. While these have been studied in single modality transformers, their cross-modal impact in Large Visi…
Bridging the gap to real-world language-grounded visual concept learning
Whie Jung, Semin Kim, Junee Kim +1
Human intelligence effortlessly interprets visual scenes along a rich spectrum of semantic dimensions. However, existing approaches to language-grounded visual concept learning are…
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Whie Jung, Jaehoon Yoo, Sungjin Ahn +1
Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning. However, mo…
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Donggyun Kim, Seongwoong Cho, Semin Kim +2
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient…