13 citations · 28 across the 7 of their papers we have counts for
7 papers · 1 filter
Learning Hierarchical Image Segmentation For Recognition and By Recognition
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…
Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers
Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo +2
Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping…
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
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, label…
Adversarial Structure Matching for Structured Prediction Tasks
Jyh-Jing Hwang, Tsung-Wei Ke, Jianbo Shi +1
Pixel-wise losses, e.g., cross-entropy or L2, have been widely used in structured prediction tasks as a spatial extension of generic image classification or regression. However, it…
Adaptive Affinity Fields for Semantic Segmentation
Tsung-Wei Ke, Jyh-Jing Hwang, Ziwei Liu +1
Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative…
Multigrid Neural Architectures
Tsung-Wei Ke, Michael Maire, Stella X. Yu
We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate acros…
Implicit Sparse Code Hashing
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. D…