Search Results for author:
Found 12 papers, 2 papers with code
Domain-aware Triplet loss in Domain Generalization
Unlike previous methods focusing on distribution alignment, our algorithm is designed to disperse domain information in the embedding space.
Ranked #3 on
Domain Generalization
on PACS
End to End Generative Meta Curriculum Learning For Medical Data Augmentation
The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style.
Conditioned Generative Transformers for Histopathology Image Synthetic Augmentation
Extensive experiments on the histopathology datasets show that leveraging our synthetic augmentation framework results in significant and consistent improvements in classification performance.
FaceCook: Face Generation Based on Linear Scaling Factors
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images.
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement
Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications.
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification
The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems.
Deep inspection: an electrical distribution pole parts study via deep neural networks
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
Towards Miss Universe Automatic Prediction: The Evening Gown Competition
Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition?
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors
The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows.
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation).
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds.
