6 papers · 1 filter
Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
Pardis Taghavi, Reza Langari, Gaurav Pandey
Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries shari…
MODEST: Multi-Optics Depth-of-Field Stereo Dataset
Nisarg K. Trivedi, Vinayak A. Belludi, Vinayaka A. Belludi +1
Training and evaluation of state-of-the-art computer vision algorithms for reliable shallow depth of field (DoF) rendering and defocus deblurring remain constrained by a persistent…
Training a Student Expert via Semi-Supervised Foundation Model Distillation
Pardis Taghavi, Tian Liu, Renjie Li +2
Foundation models deliver strong perception but are often too computationally heavy to deploy, and adapting them typically requires costly annotations. We introduce a semi-supervis…
The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
Xiangbo Gao, Mingyang Wu, Siyuan Yang +4
While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time…
CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Pardis Taghavi, Tian Liu, Renjie Li +2
Instance segmentation demands costly per-pixel annotations and computationally expensive models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that c…
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images
Pardis Taghavi, Reza Langari, Gaurav Pandey
This research paper presents an innovative multi-task learning framework that allows concurrent depth estimation and semantic segmentation using a single camera. The proposed appro…