478 citations · 1.5k across the 97 of their papers we have counts for
13 papers · 1 filter
Retrieval-Augmented Transformer for Image Captioning
Sara Sarto, Marcella Cornia, Lorenzo Baraldi +1
Image captioning models aim at connecting Vision and Language by providing natural language descriptions of input images. In the past few years, the task has been tackled by learni…
Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions
Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi +1
Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and…
The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition
Silvia Cascianelli, Vittorio Pippi, Martin Maarand +4
Handwritten Text Recognition (HTR) is an open problem at the intersection of Computer Vision and Natural Language Processing. The main challenges, when dealing with historical manu…
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
Aniello Panariello, Angelo Porrello, Simone Calderara +1
This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available d…
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
Nicola Messina, Matteo Stefanini, Marcella Cornia +4
Image-text matching is gaining a leading role among tasks involving the joint understanding of vision and language. In literature, this task is often used as a pre-training objecti…
Maximum Class Separation as Inductive Bias in One Matrix
Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler +3
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not…
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
Bin Ren, Yahui Liu, Yue Song +4
Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs…
Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction
Luigi Filippo Chiara, Pasquale Coscia, Sourav Das +3
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires k…
Embodied Navigation at the Art Gallery
Roberto Bigazzi, Federico Landi, Silvia Cascianelli +3
Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and e…
Spot the Difference: A Novel Task for Embodied Agents in Changing Environments
Federico Landi, Roberto Bigazzi, Marcella Cornia +3
Embodied AI is a recent research area that aims at creating intelligent agents that can move and operate inside an environment. Existing approaches in this field demand the agents…
Dress Code: High-Resolution Multi-Category Virtual Try-On
Davide Morelli, Matteo Fincato, Marcella Cornia +3
Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts,…
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting
Alessio Monti, Angelo Porrello, Simone Calderara +3
Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracke…
CaMEL: Mean Teacher Learning for Image Captioning
Manuele Barraco, Matteo Stefanini, Marcella Cornia +3
Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities. In this paper we present CaMEL,…