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Found 14 papers, 6 papers with code
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
Existing self-training methods use a model trained on labeled source data to generate pseudo labels for target data and refine the predictions via fine-tuning the network on the pseudo labels.
CAFA: Class-Aware Feature Alignment for Test-Time Adaptation
That is, a model does not have a chance to learn test data in a class-discriminative manner, which was feasible in other adaptation tasks (\textit{e. g.,} unsupervised domain adaptation) via supervised losses on the source data.
Few-shot Weakly-Supervised Object Detection via Directional Statistics
When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images.
A Robotic 3D Perception System for Operating Room Environment Awareness
Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness.
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms.
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy.
MMTM: Multimodal Transfer Module for CNN Fusion
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
Ranked #3 on
Hand Gesture Recognition
on NVGesture
Learning to Find Common Objects Across Few Image Collections
We model the selection as an energy minimization problem with unary and pairwise potential functions.
Truncated Back-propagation for Bilevel Optimization
Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks.
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics.
One-Shot Learning for Semantic Segmentation
Low-shot learning methods for image classification support learning from sparse data.
Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm.
Local Similarities, Global Coding: An Algorithm for Feature Coding and its Applications
Data coding as a building block of several image processing algorithms has been received great attention recently.
From Local Similarity to Global Coding: An Application to Image Classification
Exploiting the local similarity of a descriptor and its nearby bases, a global measure of association of a descriptor to all the bases is computed.
