42 citations · 96 across the 14 of their papers we have counts for
15 papers
SALON: Self-supervised Adaptive Learning for Off-road Navigation
Matthew Sivaprakasam, Samuel Triest, Cherie Ho +5
Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse sc…
FAMOUS: High-Fidelity Monocular 3D Human Digitization Using View Synthesis
Vishnu Mani Hema, Shubhra Aich, Christian Haene +2
The advancement in deep implicit modeling and articulated models has significantly enhanced the process of digitizing human figures in 3D from just a single image. While state-of-t…
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari +6
High-speed off-road navigation requires long-range, high-resolution maps to enable robots to safely navigate over different surfaces while avoiding dangerous obstacles. However, du…
PIAug -- Physics Informed Augmentation for Learning Vehicle Dynamics for Off-Road Navigation
Parv Maheshwari, Wenshan Wang, Samuel Triest +5
Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Re…
Using Large Text-to-Image Models with Structured Prompts for Skin Disease Identification: A Case Study
Sajith Rajapaksa, Jean Marie Uwabeza Vianney, Renell Castro +2
This paper investigates the potential usage of large text-to-image (LTI) models for the automated diagnosis of a few skin conditions with rarity or a serious lack of annotated data…
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
Eduardo R. Corral-Soto, Mrigank Rochan, Yannis Y. He +3
In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source dom…
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
Mrigank Rochan, Shubhra Aich, Eduardo R. Corral-Soto +2
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance o…
Bidirectional Attention Network for Monocular Depth Estimation
Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam +2
In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integ…
Multi-Scale Weight Sharing Network for Image Recognition
Shubhra Aich, Ian Stavness, Yasuhiro Taniguchi +1
In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks. Inspired by traditional computer vision approaches, we share the weights of con…
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving
Jean Marie Uwabeza Vianney, Shubhra Aich, Bingbing Liu
In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without mu…
Semantic Binary Segmentation using Convolutional Networks without Decoders
Shubhra Aich, William van der Kamp, Ian Stavness
In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder f…
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images
Shubhra Aich, Ian Stavness
In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images.…
Improving Object Counting with Heatmap Regulation
Shubhra Aich, Ian Stavness
In this paper, we propose a simple and effective way to improve one-look regression models for object counting from images. We use class activation map visualizations to illustrate…
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov +6
In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional networ…
Leaf Counting with Deep Convolutional and Deconvolutional Networks
Shubhra Aich, Ian Stavness
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task gene…