2 citations · 5 across the 7 of their papers we have counts for
8 papers
Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles
Dong Lao, Yuxiang Zhang, Haniyeh Ehsani Oskouie +3
We propose a test-time defense mechanism against adversarial attacks: imperceptible image perturbations that significantly alter the predictions of a model. Unlike existing methods…
STree: Speculative Tree Decoding for Hybrid State-Space Models
Yangchao Wu, Zongyue Qin, Alex Wong +1
Speculative decoding is a technique to leverage hardware concurrency in order to enable multiple steps of token generation in a single forward pass, thus improving the efficiency o…
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
Ziyao Zeng, Yangchao Wu, Hyoungseob Park +6
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection du…
Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation
Dong Lao, Congli Wang, Alex Wong +1
We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. Since supervised data is often technically impossible to o…
WorDepth: Variational Language Prior for Monocular Depth Estimation
Ziyao Zeng, Daniel Wang, Fengyu Yang +6
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly…
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
Dong Lao, Zhengyang Hu, Francesco Locatello +2
We investigate the emergence of objects in visual perception in the absence of any semantic annotation. The resulting model has received no supervision, does not use any pre-traine…
On the Viability of Monocular Depth Pre-training for Semantic Segmentation
Dong Lao, Fengyu Yang, Daniel Wang +4
The question of whether pre-training on geometric tasks is viable for downstream transfer to semantic tasks is important for two reasons, one practical and the other scientific. If…
Detachable Object Detection: Segmentation and Depth Ordering From Short-Baseline Video
Alper Ayvaci, Stefano Soatto
We describe an approach for segmenting an image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and…