1.9k citations · 2.6k across the 51 of their papers we have counts for
9 papers · 1 filter
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
Rongkai Ma, Pengfei Fang, Tom Drummond +1
Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learnin…
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Rongkai Ma, Pengfei Fang, Gil Avraham +4
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot l…
Learning Online for Unified Segmentation and Tracking Models
Tianyu Zhu, Rongkai Ma, Mehrtash Harandi +1
Tracking requires building a discriminative model for the target in the inference stage. An effective way to achieve this is online learning, which can comfortably outperform model…
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
Lie Ju, Xin Wang, Lin Wang +5
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which r…
Seeing Thru Walls: Visualizing Mobile Robots in Augmented Reality
Morris Gu, Akansel Cosgun, Wesley P. Chan +2
We present an approach for visualizing mobile robots through an Augmented Reality headset when there is no line-of-sight visibility between the robot and the human. Three elements…
Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface
Benjamin Waymouth, Akansel Cosgun, Rhys Newbury +4
An appropriate user interface to collect human demonstration data for deformable object manipulation has been mostly overlooked in the literature. We present an interaction design…
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Lie Ju, Xin Wang, Lin Wang +6
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognit…
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tianyu Zhu, Markus Hiller, Mahsa Ehsanpour +4
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able t…
Visualizing Robot Intent for Object Handovers with Augmented Reality
Rhys Newbury, Akansel Cosgun, Tysha Crowley-Davis +3
Humans are highly skilled in communicating their intent for when and where a handover would occur. However, even the state-of-the-art robotic implementations for handovers typicall…