1 code implementation • 4 Apr 2024 • Ziyao Zeng, Daniel Wang, Fengyu Yang, Hyoungseob Park, Yangchao Wu, Stefano Soatto, Byung-Woo Hong, Dong Lao, Alex Wong
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
no code implementations • 15 Oct 2023 • Yangchao Wu, Tian Yu Liu, Hyoungseob Park, Stefano Soatto, Dong Lao, Alex Wong
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
no code implementations • 6 Oct 2023 • Dong Lao, Yangchao Wu, Tian Yu Liu, Alex Wong, Stefano Soatto
We term our method ``Stochastic Resonance Transformer" (SRT), which we show can effectively super-resolve features of pre-trained ViTs, capturing more of the local fine-grained structures that might otherwise be neglected as a result of tokenization.
1 code implementation • 14 Nov 2022 • Alexandre Tiard, Alex Wong, David Joon Ho, Yangchao Wu, Eliram Nof, Alvin C. Goh, Stefano Soatto, Saad Nadeem
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS) to HER2 status classification and treatment response prediction.
1 code implementation • 18 Sep 2021 • Alex Wong, Allison Chen, Yangchao Wu, Safa Cicek, Alexandre Tiard, Byung-Woo Hong, Stefano Soatto
We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.