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Jonatas Wehrmann

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AAAI Conference 2020 Conference Paper

Adaptive Cross-Modal Embeddings for Image-Text Alignment

Jonatas Wehrmann
Camila Kolling
Rodrigo C Barros

In this paper, we introduce a novel approach for training image-text alignment models, namely ADAPT. Image-text alignment methods are often used for cross-modal retrieval, i. e. , to retrieve an image given a query text, or captions that successfully label an image. ADAPT is designed to adjust an intermediate representation of instances from a modality a using an embedding vector of an instance from modality b. Such an adaptation is designed to ﬁlter and enhance important information across internal features, allowing for guided vector representations – which resembles the working of attention modules, though far more computationally efﬁcient. Experimental results on two large-scale Image-Text alignment datasets show that ADAPT-models outperform all the baseline approaches by large margins. Particularly, for Image Retrieval, ADAPT, with a single model, outperforms the state-of-the-art approach by a relative improvement of R@1 ≈ 24% and for Image Annotation, R@1 ≈ 8% on Flickr30k dataset. On MS COCO it provides an improvement of R@1 ≈ 12% for Image Retrieval, and ≈ 7% R@1 for Image Annotation. Code is available at https: //github. com/jwehrmann/ retrieval. pytorch.

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ICML Conference 2018 Conference Paper

Hierarchical Multi-Label Classification Networks

Jonatas Wehrmann
Ricardo Cerri
Rodrigo C. Barros

One of the most challenging machine learning problems is a particular case of data classification in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy at the same time. This task is known as hierarchical multi-label classification (HMC), with applications in text classification, image annotation, and in bioinformatics problems such as protein function prediction. In this paper, we propose novel neural network architectures for HMC called HMCN, capable of simultaneously optimizing local and global loss functions for discovering local hierarchical class-relationships and global information from the entire class hierarchy while penalizing hierarchical violations. We evaluate its performance in 21 datasets from four distinct domains, and we compare it against the current HMC state-of-the-art approaches. Results show that HMCN substantially outperforms all baselines with statistical significance, arising as the novel state-of-the-art for HMC.

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