8 accepted papers
Navigating unseen environments based on natural language instructions remains difficult for egocentric agents in Vision-and-Language Navigation (VLN). Intuitively, humans inherently ground concrete semantic knowledge within spatial layouts during indoor navigation. Although previous studies have i
Diffusion models have achieved remarkable success in graph generation, but enforcing hard constraints on generated graphs remains challenging, limiting their deployment in constraint-critical applications. Existing approaches either fail to guarantee strict constraint satisfaction or are limited to …
Particle Image Velocimetry (PIV) aims to infer underlying velocity fields from time-separated particle images, forming a PDE-constrained inverse problem governed by advection dynamics. Traditional cross-correlation methods and deep learning-based feature matching approaches often struggle with ambig…
Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale manifested as over-smoothness or instability at small CFG scales while over-saturation at large ones. To explain and analyze these issues we revisit the derivation of Score Distillation Sampling (SDS) and decip…
Current depth completion methods usually employ a pair of calibrated RGB and depth sensors to reconstruct a dense depth map. Although RGB (dense) and depth (sparse) measurements are collected from the same underlying scene, they reflect different physical characteristics and thus it remains rather i
We propose Super-resolution Neural Operator (SRNO), a deep operator learning framework that can resolve high-resolution (HR) images at arbitrary scales from the low-resolution (LR) counterparts. Treating the LR-HR image pairs as continuous functions approximated with different grid sizes, SRNO learn…
In this paper, we propose a patch-wise super-resolution (SR) method that combines an external-sample classification tree and a nonlinear-mapping learning stage to simultaneously guarantees reconstruction quality and speed at the stage of patch representation and mapping. We use the low-resolution (L…
Recent advances in compressive spectral imagers have demonstrated the potential of spatio-spectral modulation (SSM) for improved reconstruction performance. Existing SSM techniques, however, use either a color filter array or a complex optical arrangement, both of which can only provide limited modu…