Search Results for author:
Found 13 papers, 6 papers with code
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy.
Using Large Text-to-Image Models with Structured Prompts for Skin Disease Identification: A Case Study
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 datasets.
Data-Free Class-Incremental Hand Gesture Recognition
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples.
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation.
Bidirectional Attention Network for Monocular Depth Estimation
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 integrating local and global information in convolutional neural networks.
Multi-Scale Weight Sharing Network for Image Recognition
In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks.
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving
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.
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images
This generalization capability allows GSP to avoid both patchwise cancellation and overfitting by training on small patches and inference on full-resolution images as a whole.
Semantic Binary Segmentation using Convolutional Networks without Decoders
In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation.
Improving Object Counting with Heatmap Regulation
Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots.
Leaf Counting with Deep Convolutional and Deconvolutional Networks
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping.
