Search Results for author:
Found 15 papers, 8 papers with code
MuscleMap: Towards Video-based Activated Muscle Group Estimation
To make the AMGE model applicable in real-life situations, it is crucial to ensure that the model can generalize well to types of physical activities not present during training and involving new combinations of activated muscles.
Towards Improving Calibration in Object Detection Under Domain Shift
To this end, we first propose a new, plug-and-play, train-time calibration loss for object detection (coined as TCD).
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting.
Ranked #1 on
Thoracic Disease Classification
on ChestX-ray14
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction
Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning.
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
In this paper, we propose to leverage model predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Ranked #1 on
Action Segmentation
on Breakfast
(mIoU metric)
Anchor-free Small-scale Multispectral Pedestrian Detection
In this way, we can both simplify the network architecture and achieve higher detection performance, especially for pedestrians under occlusion or at low object resolution.
Clustering based Contrastive Learning for Improving Face Representations
We demonstrate our method on the challenging task of learning representations for video face clustering.
Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
In this paper, we propose a novel method of computing the loss directly between the source and target images that enable proper distillation of shape/content and colour/style.
Self-Supervised Learning of Face Representations for Video Face Clustering
In this paper, we address video face clustering using unsupervised methods.
Efficient Parameter-free Clustering Using First Neighbor Relations
We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data.
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation.
Ranked #17 on
Person Re-Identification
on MARS
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian.
Deep Perceptual Mapping for Cross-Modal Face Recognition
Our method bridges the drop in performance due to the modality gap by more than 40\%.
Ranked #2 on
Face Recognition
on UND-X1
Deep Perceptual Mapping for Thermal to Visible Face Recognition
Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications.
