176 citations · 699 across the 41 of their papers we have counts for
21 papers · 1 filter
All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators
Brijen Thananjeyan, Justin Kerr, Huang Huang +2
Large-scale semantic image annotation is a significant challenge for learning-based perception systems in robotics. Current approaches often rely on human labelers, which can be ex…
On Guiding Visual Attention with Language Specification
Suzanne Petryk, Lisa Dunlap, Keyan Nasseri +3
While real world challenges typically define visual categories with language words or phrases, most visual classification methods define categories with numerical indices. However,…
Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation
Ruizhe Cheng, Bichen Wu, Peizhao Zhang +2
Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of super…
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Xin Wang, Thomas E. Huang, Benlin Liu +4
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.…
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan +5
We explore learning pixelwise correspondences between images of deformable objects in different configurations. Traditional correspondence matching approaches such as SIFT, SURF, a…
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang +8
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively e…