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Jovana Mitrovic

Alumni

Publications

Improving fine-grained understanding in image-text pre-training
Ioana Bica
Anastasija Ili'c
Matthias Bauer
Goker Erdogan
Matko Bovsnjak
Christos Kaplanis
Alexey A. Gritsenko
Matthias Minderer
Charles Blundell
Razvan Pascanu
Jovana Mitrovic
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations… (voir plus) from image-text pairs. Given that multiple image patches often correspond to single words, we propose to learn a grouping of image patches for every token in the caption. To achieve this, we use a sparse similarity metric between image patches and language tokens and compute for each token a language-grouped vision embedding as the weighted average of patches. The token and language-grouped vision embeddings are then contrasted through a fine-grained sequence-wise loss that only depends on individual samples and does not require other batch samples as negatives. This enables more detailed information to be learned in a computationally inexpensive manner. SPARC combines this fine-grained loss with a contrastive loss between global image and text embeddings to learn representations that simultaneously encode global and local information. We thoroughly evaluate our proposed method and show improved performance over competing approaches both on image-level tasks relying on coarse-grained information, e.g. classification, as well as region-level tasks relying on fine-grained information, e.g. retrieval, object detection, and segmentation. Moreover, SPARC improves model faithfulness and captioning in foundational vision-language models.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
arxiv.org
Continually learning representations at scale
Alexandre Galashov
Jovana Mitrovic
Dhruva Tirumala
Yee Whye Teh
Timothy Nguyen
Arslan Chaudhry
Razvan Pascanu
2023-01-01
CoLLAs (publié)
proceedings.mlr.press
Hierarchical Adversarially Learned Inference
Ishmael Belghazi
Sai Rajeswar
Olivier Mastropietro
Negar Rostamzadeh
Jovana Mitrovic
Aaron Courville
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative … (voir plus)and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity. Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA. Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.
2018-02-04
ArXiv (prépublication)
openreview.net
openreview.net