1 code implementation • 16 Feb 2024 • Tsung-Wei Ke, Nikolaos Gkanatsios, Katerina Fragkiadaki
We marry diffusion policies and 3D scene representations for robot manipulation.
no code implementations • 9 Feb 2024 • Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
1 code implementation • 27 Nov 2023 • Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki
Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model.
1 code implementation • 1 Oct 2022 • Tsung-Wei Ke, Sangwoo Mo, Stella X. Yu
Large vision and language models learned directly through image-text associations often lack detailed visual substantiation, whereas image segmentation tasks are treated separately from recognition, supervisedly learned without interconnections.
1 code implementation • CVPR 2022 • Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, Stella X. Yu
We enforce spatial consistency of grouping and bootstrap feature learning with co-segmentation among multiple views of the same image, and enforce semantic consistency across the grouping hierarchy with clustering transformers between coarse- and fine-grained features.
1 code implementation • ICLR 2021 • Tsung-Wei Ke, Jyh-Jing Hwang, Stella X. Yu
Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles.
no code implementations • 1 Jan 2021 • Jyh-Jing Hwang, Tsung-Wei Ke, Stella Yu
We aim to leverage the densely labeled task, image parsing, a. k. a panoptic segmentation, to learn a model that encodes and discovers object-centric context.
1 code implementation • CVPR 2019 • Jyh-Jing Hwang, Tsung-Wei Ke, Jianbo Shi, Stella X. Yu
The structure analyzer is trained to maximize the ASM loss, or to emphasize recurring multi-scale hard negative structural mistakes among co-occurring patterns.
1 code implementation • ECCV 2018 • Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu, Stella X. Yu
Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks (GAN).
Ranked #55 on Semantic Segmentation on Cityscapes test
1 code implementation • CVPR 2017 • Tsung-Wei Ke, Michael Maire, Stella X. Yu
Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail.
no code implementations • 1 Dec 2015 • Tsung-Yu Lin, Tsung-Wei Ke, Tyng-Luh Liu
We address the problem of converting large-scale high-dimensional image data into binary codes so that approximate nearest-neighbor search over them can be efficiently performed.