15 papers · 1 filter
Future Optical Flow Prediction Improves Robot Control & Video Generation
Kanchana Ranasinghe, Honglu Zhou, Yu Fang +7
Future motion representations, such as optical flow, offer immense value for control and generative tasks. However, forecasting generalizable spatially dense motion representations…
CoPT: Unsupervised Domain Adaptive Segmentation using Domain-Agnostic Text Embeddings
Cristina Mata, Kanchana Ranasinghe, Michael S. Ryoo
Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particul…
Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation
Ulindu De Silva, Didula Samaraweera, Sasini Wanigathunga +4
We present Seg-TTO, a novel framework for zero-shot, open-vocabulary semantic segmentation (OVSS), designed to excel in specialized domain tasks. While current open-vocabulary appr…
LatentCRF: Continuous CRF for Efficient Latent Diffusion
Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit +5
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability.…
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA
Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya +3
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefor…
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
Kanchana Ranasinghe, Satya Narayan Shukla, Omid Poursaeed +2
Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly…