9 citations · 19 across the 11 of their papers we have counts for
13 papers
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement Learning
Antoine Chaffin, Ewa Kijak, Vincent Claveau
Training image captioning models using teacher forcing results in very generic samples, whereas more distinctive captions can be very useful in retrieval applications or to produce…
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup refers to interpolation-based data augmentation, originally motivated as a way to go beyond empirical risk minimization (ERM). Its extensions mostly focus on the definition o…
Building Flyweight FLIM-based CNNs with Adaptive Decoding for Object Detection
Leonardo de Melo Joao, Azael de Melo e Sousa, Bianca Martins dos Santos +4
State-of-the-art (SOTA) object detection methods have succeeded in several applications at the price of relying on heavyweight neural networks, which makes them inefficient and inv…
Teach me how to Interpolate a Myriad of Embeddings
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup refers to interpolation-based data augmentation, originally motivated as a way to go beyond empirical risk minimization (ERM). Yet, its extensions focus on the definition of…
Which Discriminator for Cooperative Text Generation?
Antoine Chaffin, Thomas Scialom, Sylvain Lamprier +4
Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external informat…
Generative Cooperative Networks for Natural Language Generation
Sylvain Lamprier, Thomas Scialom, Antoine Chaffin +4
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outp…
Generating artificial texts as substitution or complement of training data
Vincent Claveau, Antoine Chaffin, Ewa Kijak
The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised l…
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Antoine Chaffin, Vincent Claveau, Ewa Kijak
Large language models (LM) based on Transformers allow to generate plausible long texts. In this paper, we explore how this generation can be further controlled at decoding time to…
It Takes Two to Tango: Mixup for Deep Metric Learning
Shashanka Venkataramanan, Bill Psomas, Ewa Kijak +3
Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pus…
AlignMixup: Improving Representations By Interpolating Aligned Features
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent m…
Detecting Human-Object Interaction with Mixed Supervision
Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak
Human object interaction (HOI) detection is an important task in image understanding and reasoning. It is in a form of HOI triplet <human; verb; object>, requiring bounding boxes f…
Unsupervised part learning for visual recognition
Ronan Sicre, Yannis Avrithis, Ewa Kijak +1
Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promisin…
Automatic discovery of discriminative parts as a quadratic assignment problem
Ronan Sicre, Julien Rabin, Yannis Avrithis +2
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses t…