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Found 16 papers, 12 papers with code
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Ranked #1 on
2D Semantic Segmentation
on xBD
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
3D Human Pose Estimation with Spatial and Temporal Transformers
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation.
Consistency-based Active Learning for Object Detection
Unlike most recent works that focused on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between original and augmented data.
Deep Learning-Based Human Pose Estimation: A Survey
Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included.
VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval
In this paper, we redefine this problem with a more realistic assumption that the query image can be arbitrary in the area of interest and the reference images are captured before the queries emerge.
A3D: Adaptive 3D Networks for Video Action Recognition
Even under the same computational constraints, the performance of our adaptive networks can be significantly boosted over the baseline counterparts by the mutual training along three dimensions.
Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection
To this end, we rethink long-tailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images.
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
Fast and effective responses are required when a natural disaster (e. g., earthquake, hurricane, etc.)
Ranked #2 on
2D Semantic Segmentation
on xBD
GradAug: A New Regularization Method for Deep Neural Networks
The key idea is utilizing randomly transformed training samples to regularize a set of sub-networks, which are originated by sampling the width of the original network, in the training process.
Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently.
Density Map Guided Object Detection in Aerial Images
Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime.
Visual Explanation for Deep Metric Learning
This work explores the visual explanation for deep metric learning and its applications.
A closer look at network resolution for efficient network design
We propose a framework to mutually learn from different input resolutions and network widths.
