Hope you are all safe and healthy, the Priberam Machine Learning Seminars will
continue to take place remotely via zoom on Tuesdays at 1 p.m.
Next Tuesday, May 26th,
Beatriz Ferreira, an IST / ISR / NETSyS Ph.D student will present her work on "Exploring Label Structure and Spatial Attention for Fashion Images Classification" at
13:00h (zoom link: https://zoom.us/j/84825474647
You can register for this event and keep watch on future seminars below:
Food will not be provided but feel free to eat at the same time :) Please note that the seminar is limited to 100 people
and this will work on a 1st come 1st served basis. So please try to be on time if you wish to attend.
Priberam is hiring!
If you are interested in working with us please consult the available positions at priberam.com/careers.
Priberam Machine Learning Lunch Seminar
Beatriz Ferreira (IST / ISR)
May 26th, 2020
Label Structure and Spatial Attention for Fashion Images Classification
In order to make
decisions, for instance when purchasing a product, people rely on rich and accurate descriptions, which entail multi-label retrieval processes. However, multi-label classification is challenged by high dimensional and complex feature spaces and its dependency
on large and accurately annotated datasets. Deep learning approaches brought a definite breakthrough in performance across numerous machine learning problems, and image classification was, undoubtedly, one of the tasks where these approaches had greater repercussions.
In this presentation we will focus on image classification of fashion images, using deep learning approaches to tackle the multi-class/multi-label problems in order to generate rich images descriptions. Fashion datasets are challenging because they include
a vast amount of similarly looking images and they are annotated with a large diversity of attributes but with few labels per exemplar. To address the previous issues we explore domain knowledge to constrain the (otherwise completely data-driven) solutions.
Specifically, we first show how to incorporate knowledge about annotations structure. Secondly, we use context and semantic localization to guide an attention mechanism that designs the feature space by focusing on visually meaningful regions.We show with
thorough experimentation the performance gains achieved for both cases.
Ferreira is a PhD student of the NETSyS program, from the Signal and Image Processing Group at Instituto de Sistemas e Robótica. Her main research interests lie in the intersection of Computer Vision and Machine Learning. She is also an apologist of interpretable
models, as she deems interpretability to be fundamental to the development of richer and more robust models, more easily comprehended by humans. She has been a PhD student intern at Farfetch and a visiting scholar at CMU. Some of her recent publications can
be found on KDD and on ICCV workshops.