1.2k citations · 2.7k across the 112 of their papers we have counts for
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MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Yingtian Zou, Vikas Verma, Sarthak Mittal +6
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.…
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
Hao Dong, Weihao Gu, Xianjing Zhang +5
High-definition (HD) semantic map generation of the environment is an essential component of autonomous driving. Existing methods have achieved good performance in this task by fus…
Expansion of Visual Hints for Improved Generalization in Stereo Matching
Andrea Pilzer, Yuxin Hou, Niki Loppi +2
We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vis…
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Yi Zhao, Rinu Boney, Alexander Ilin +2
Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the qua…
Continuous Monte Carlo Graph Search
Kalle Kujanpää, Amin Babadi, Yi Zhao +3
Online planning is crucial for high performance in many complex sequential decision-making tasks. Monte Carlo Tree Search (MCTS) employs a principled mechanism for trading off expl…
Uncertainty-guided Source-free Domain Adaptation
Subhankar Roy, Martin Trapp, Andrea Pilzer +4
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data a…
Visual Localization via Few-Shot Scene Region Classification
Siyan Dong, Shuzhe Wang, Yixin Zhuang +3
Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of m…
Disentangling Model Multiplicity in Deep Learning
Ari Heljakka, Martin Trapp, Juho Kannala +1
Model multiplicity is a well-known but poorly understood phenomenon that undermines the generalisation guarantees of machine learning models. It appears when two models with simila…
Multiple Offsets Multilateration: a new paradigm for sensor network calibration with unsynchronized reference nodes
Luca Ferranti, Kalle Åström, Magnus Oskarsson +2
Positioning using wave signal measurements is used in several applications, such as GPS systems, structure from sound and Wifi based positioning. Mathematically, such problems requ…