492 citations · 1.5k across the 62 of their papers we have counts for
9 papers · 2 filters
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…
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul +4
The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory…
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…
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud
Mong H. Ng, Kaahan Radia, Jianfei Chen +3
Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in t…
SegNBDT: Visual Decision Rules for Segmentation
Alvin Wan, Daniel Ho, Younjin Song +3
The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks li…
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
Xiaoliang Dai, Alvin Wan, Peizhao Zhang +8
Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for archite…