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Found 8 papers, 4 papers with code
Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
Partially annotated clips contain rich temporal contexts that can complement the sparse key frame annotations in providing supervision for model training.
Decentralized Bayesian Learning over Graphs
The proposed algorithm can be viewed as a Bayesian and peer-to-peer variant of federated learning in which each agent keeps a "posterior probability distribution" over a global model parameters.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error).
Ranked #61 on
Monocular Depth Estimation
on KITTI Eigen split
Efficient Video Understanding via Layered Multi Frame-Rate Analysis
One of the greatest challenges in the design of a real-time perception system for autonomous driving vehicles and drones is the conflicting requirement of safety (high prediction accuracy) and efficiency.
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them.
S3Pool: Pooling with Stochastic Spatial Sampling
We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e. g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e. g., top-left) manner.
Adaptive Object Detection Using Adjacency and Zoom Prediction
Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small.
Efficient Object Detection for High Resolution Images
Efficient generation of high-quality object proposals is an essential step in state-of-the-art object detection systems based on deep convolutional neural networks (DCNN) features.
