1 code implementation • 30 Oct 2024 • Alessandro D'Amelio, Giuseppe Cartella, Vittorio Cuculo, Manuele Lucchi, Marcella Cornia, Rita Cucchiara, Giuseppe Boccignone
Attention guides our gaze to fixate the proper location of the scene and holds it in that location for the deserved amount of time given current processing demands, before shifting to the next one.
1 code implementation • 23 Oct 2024 • Luca Barsellotti, Roberto Bigazzi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
In the last years, the research interest in visual navigation towards objects in indoor environments has grown significantly.
1 code implementation • 9 Oct 2024 • Sara Sarto, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Metrics can indeed play a key role in the fine-tuning stage of captioning models, ultimately enhancing the quality of the generated captions.
no code implementations • 16 Sep 2024 • Federico Betti, Lorenzo Baraldi, Rita Cucchiara, Nicu Sebe
Diffusion models have significantly advanced generative AI, but they encounter difficulties when generating complex combinations of multiple objects.
no code implementations • 9 Sep 2024 • Davide Di Nucci, Alessandro Simoni, Matteo Tomei, Luca Ciuffreda, Roberto Vezzani, Rita Cucchiara
The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the diffusion of NeRF-based approaches.
no code implementations • 29 Aug 2024 • Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Fine-tuning image captioning models with hand-crafted rewards like the CIDEr metric has been a classical strategy for promoting caption quality at the sequence level.
2 code implementations • 28 Aug 2024 • Fabio Quattrini, Carmine Zaccagnino, Silvia Cascianelli, Laura Righi, Rita Cucchiara
Regesta are catalogs of summaries of other documents and, in some cases, are the only source of information about the content of such full-length documents.
1 code implementation • 28 Aug 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
To overcome this limitation, we propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting to improve the perceptual and semantical coherence of the generated panorama images.
1 code implementation • 27 Aug 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Designs and artworks are ubiquitous across various creative fields, requiring graphic design skills and dedicated software to create compositions that include many graphical elements, such as logos, icons, symbols, and art scenes, which are integral to visual storytelling.
1 code implementation • 26 Aug 2024 • Nicholas Moratelli, Davide Caffagni, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
However, when attempting to optimize modern and higher-quality metrics like CLIP-Score and PAC-Score, this training method often encounters instability and fails to acquire the genuine descriptive capabilities needed to produce fluent and informative captions.
1 code implementation • 29 Jul 2024 • Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi, Rita Cucchiara
To sustain the training of our model, we generate a comprehensive dataset that focuses on images generated by diffusion models and encompasses a collection of 9. 2 million images produced by using four different generators.
1 code implementation • 29 Jul 2024 • Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Effectively aligning with human judgment when evaluating machine-generated image captions represents a complex yet intriguing challenge.
1 code implementation • 8 Jul 2024 • Bin Ren, Guofeng Mei, Danda Pani Paudel, Weijie Wang, Yawei Li, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Nicu Sebe
To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance.
1 code implementation • 1 Jul 2024 • Matteo Mosconi, Andriy Sorokin, Aniello Panariello, Angelo Porrello, Jacopo Bonato, Marco Cotogni, Luigi Sabetta, Simone Calderara, Rita Cucchiara
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively.
no code implementations • 31 May 2024 • Riccardo Benaglia, Angelo Porrello, Pietro Buzzega, Simone Calderara, Rita Cucchiara
Trajectory forecasting is crucial for video surveillance analytics, as it enables the anticipation of future movements for a set of agents, e. g. basketball players engaged in intricate interactions with long-term intentions.
no code implementations • 30 May 2024 • Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Ming-Hsuan Yang, Nicu Sebe
Additionally, for IR, it is commonly noted that small segments of a degraded image, particularly those closely aligned semantically, provide particularly relevant information to aid in the restoration process, as they contribute essential contextual cues crucial for accurate reconstruction.
no code implementations • 25 May 2024 • Angelo Porrello, Lorenzo Bonicelli, Pietro Buzzega, Monica Millunzi, Simone Calderara, Rita Cucchiara
The fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model.
no code implementations • 21 May 2024 • Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi, Rita Cucchiara
The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images.
1 code implementation • 26 Apr 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Document Image Binarization is a well-known problem in Document Analysis and Computer Vision, although it is far from being solved.
no code implementations • 23 Apr 2024 • Davide Caffagni, Federico Cocchi, Nicholas Moratelli, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality.
no code implementations • 15 Apr 2024 • Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi, Rita Cucchiara
VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal.
no code implementations • CVPR 2024 • Luca Barsellotti, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form.
Open Vocabulary Semantic Segmentation Open-Vocabulary Semantic Segmentation +1
2 code implementations • 21 Mar 2024 • Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini, Rita Cucchiara
Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body.
1 code implementation • 13 Mar 2024 • Giuseppe Cartella, Vittorio Cuculo, Marcella Cornia, Rita Cucchiara
Creating high-quality and realistic images is now possible thanks to the impressive advancements in image generation.
no code implementations • 11 Mar 2024 • Roberto Bigazzi, Lorenzo Baraldi, Shreyas Kousik, Rita Cucchiara, Marco Pavone
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments.
1 code implementation • 28 Feb 2024 • Giuseppe Cartella, Marcella Cornia, Vittorio Cuculo, Alessandro D'Amelio, Dario Zanca, Giuseppe Boccignone, Rita Cucchiara
Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence models that aim to solve problems in various domains, including image and video processing, vision-and-language applications, and language modelling.
no code implementations • 19 Feb 2024 • Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara
Connecting text and visual modalities plays an essential role in generative intelligence.
no code implementations • 16 Feb 2024 • Bram Vanherle, Vittorio Pippi, Silvia Cascianelli, Nick Michiels, Frank Van Reeth, Rita Cucchiara
Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and, preliminarily, Diffusion Models.
no code implementations • 4 Feb 2024 • Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Nicu Sebe
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution.
no code implementations • 6 Jan 2024 • Aniello Panariello, Gianluca Mancusi, Fedy Haj Ali, Angelo Porrello, Simone Calderara, Rita Cucchiara
Existing approaches rely on two scales: local information (i. e., the bounding box proportions) or global information, which encodes the semantics of the scene as well as the spatial relations with neighboring objects.
1 code implementation • 27 Nov 2023 • Samuele Poppi, Tobia Poppi, Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
We show how this can be done by fine-tuning a CLIP model on synthetic data obtained from a large language model trained to convert between safe and unsafe sentences, and a text-to-image generator.
1 code implementation • 31 Oct 2023 • Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Rita Cucchiara
Through extensive experimental evaluation on different word-level and line-level datasets of handwritten text images, we demonstrate the suitability of the proposed HWD as a score for Styled HTG.
2 code implementations • 11 Sep 2023 • Giuseppe Cartella, Alberto Baldrati, Davide Morelli, Marcella Cornia, Marco Bertini, Rita Cucchiara
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements.
1 code implementation • ICCV 2023 • Manuele Barraco, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Image captioning, like many tasks involving vision and language, currently relies on Transformer-based architectures for extracting the semantics in an image and translating it into linguistically coherent descriptions.
no code implementations • ICCV 2023 • Gianluca Mancusi, Aniello Panariello, Angelo Porrello, Matteo Fabbri, Simone Calderara, Rita Cucchiara
The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches.
1 code implementation • 9 Aug 2023 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Recent advancements in Digital Document Restoration (DDR) have led to significant breakthroughs in analyzing highly damaged written artifacts.
no code implementations • 24 Jul 2023 • Davide Di Nucci, Alessandro Simoni, Matteo Tomei, Luca Ciuffreda, Roberto Vezzani, Rita Cucchiara
Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images.
no code implementations • 18 Jul 2023 • Federico Betti, Jacopo Staiano, Lorenzo Baraldi, Rita Cucchiara, Nicu Sebe
Research in Image Generation has recently made significant progress, particularly boosted by the introduction of Vision-Language models which are able to produce high-quality visual content based on textual inputs.
1 code implementation • 12 Jun 2023 • Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, Andrea Pilzer, Rita Cucchiara
The use of self-supervised pre-training has emerged as a promising approach to enhance the performance of visual tasks such as image classification.
1 code implementation • 22 May 2023 • Davide Morelli, Alberto Baldrati, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara
In this context, image-based virtual try-on, which consists in generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of these powerful generative solutions.
no code implementations • 4 May 2023 • Vittorio Pippi, Silvia Cascianelli, Christopher Kermorvant, Rita Cucchiara
Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets.
no code implementations • 4 Apr 2023 • Samuele Poppi, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network.
1 code implementation • ICCV 2023 • Alberto Baldrati, Davide Morelli, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara
Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner.
no code implementations • 4 Apr 2023 • Vittorio Pippi, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks.
2 code implementations • 2 Apr 2023 • Roberto Amoroso, Davide Morelli, Marcella Cornia, Lorenzo Baraldi, Alberto del Bimbo, Rita Cucchiara
Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language.
1 code implementation • CVPR 2023 • Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Generating synthetic images of handwritten text in a writer-specific style is a challenging task, especially in the case of unseen styles and new words, and even more when these latter contain characters that are rarely encountered during training.
1 code implementation • CVPR 2023 • Sara Sarto, Manuele Barraco, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
The CLIP model has been recently proven to be very effective for a variety of cross-modal tasks, including the evaluation of captions generated from vision-and-language architectures.
1 code implementation • 13 Feb 2023 • Simone Luetto, Fabrizio Garuti, Enver Sangineto, Lorenzo Forni, Rita Cucchiara
There is a recent growing interest in applying Deep Learning techniques to tabular data, in order to replicate the success of other Artificial Intelligence areas in this structured domain.
1 code implementation • 27 Jan 2023 • Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara
Denoising Diffusion Probabilistic Models have shown an impressive generation quality, although their long sampling chain leads to high computational costs.
Ranked #1 on Image Generation on FFHQ 128 x 128
no code implementations • 17 Jan 2023 • Roberto Bigazzi, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
The development of embodied agents that can communicate with humans in natural language has gained increasing interest over the last years, as it facilitates the diffusion of robotic platforms in human-populated environments.
no code implementations • 17 Aug 2022 • Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
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 reuse of their content.
no code implementations • 16 Aug 2022 • Silvia Cascianelli, Vittorio Pippi, Martin Maarand, Marcella Cornia, Lorenzo Baraldi, Christopher Kermorvant, Rita Cucchiara
With the aim of fostering the research on this topic, in this paper we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years.
1 code implementation • 10 Aug 2022 • Aniello Panariello, Angelo Porrello, Simone Calderara, Rita Cucchiara
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 during training.
Ranked #12 on Anomaly Detection In Surveillance Videos on XD-Violence
Anomaly Detection In Surveillance Videos Anomaly Localization +4
1 code implementation • 29 Jul 2022 • Nicola Messina, Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Fabrizio Falchi, Giuseppe Amato, Rita Cucchiara
In literature, this task is often used as a pre-training objective to forge architectures able to jointly deal with images and texts.
Ranked #22 on Cross-Modal Retrieval on COCO 2014 (using extra training data)
no code implementations • 26 Jul 2022 • Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
In this paper, we investigate the development of an image captioning approach with a kNN memory, with which knowledge can be retrieved from an external corpus to aid the generation process.
1 code implementation • 17 Jun 2022 • Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes
This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations.
1 code implementation • CVPR 2023 • Bin Ren, Yahui Liu, Yue Song, Wei Bi, Rita Cucchiara, Nicu Sebe, Wei Wang
In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded.
no code implementations • ICIAP 2022 • Mang Ning, Xiaoliang Ma, Yao Lu, Simone Calderara, Rita Cucchiara
In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone.
1 code implementation • 25 Apr 2022 • Luigi Filippo Chiara, Pasquale Coscia, Sourav Das, Simone Calderara, Rita Cucchiara, Lamberto Ballan
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications.
1 code implementation • 19 Apr 2022 • Roberto Bigazzi, Federico Landi, Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information.
1 code implementation • 18 Apr 2022 • Davide Morelli, Matteo Fincato, Marcella Cornia, Federico Landi, Fabio Cesari, Rita Cucchiara
Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024x768) with front-view, full-body reference models.
Ranked #5 on Virtual Try-on on VITON
1 code implementation • 18 Apr 2022 • Federico Landi, Roberto Bigazzi, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
To make a step towards this setting, we propose Spot the Difference: a novel task for Embodied AI where the agent has access to an outdated map of the environment and needs to recover the correct layout in a fixed time budget.
no code implementations • CVPR 2022 • Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara
To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones).
1 code implementation • 21 Feb 2022 • Manuele Barraco, Matteo Stefanini, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities.
no code implementations • 24 Nov 2021 • Marcella Cornia, Lorenzo Baraldi, Giuseppe Fiameni, Rita Cucchiara
This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources, containing both human-annotated and web-collected captions.
1 code implementation • 21 Oct 2021 • Alessandro Simoni, Stefano Pini, Roberto Vezzani, Rita Cucchiara
Recently, learning frameworks have shown the capability of inferring the accurate shape, pose, and texture of an object from a single RGB image.
1 code implementation • 14 Sep 2021 • Roberto Bigazzi, Federico Landi, Silvia Cascianelli, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara
The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts.
no code implementations • 31 Aug 2021 • Federico Landi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara
Numerical results suggest that the cell state contains useful information that is worth including in the gate structure.
1 code implementation • ICCV 2021 • Matteo Fabbri, Guillem Braso, Gianluca Maugeri, Orcun Cetintas, Riccardo Gasparini, Aljosa Osep, Simone Calderara, Laura Leal-Taixe, Rita Cucchiara
Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance.
no code implementations • 14 Jul 2021 • Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, Rita Cucchiara
Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation.
no code implementations • 21 Jun 2021 • Ariel Caputo, Andrea Giachetti, Simone Soso, Deborah Pintani, Andrea D'Eusanio, Stefano Pini, Guido Borghi, Alessandro Simoni, Roberto Vezzani, Rita Cucchiara, Andrea Ranieri, Franca Giannini, Katia Lupinetti, Marina Monti, Mehran Maghoumi, Joseph J. LaViola Jr, Minh-Quan Le, Hai-Dang Nguyen, Minh-Triet Tran
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more.
no code implementations • 2 Jun 2021 • Marco Cagrandi, Marcella Cornia, Matteo Stefanini, Lorenzo Baraldi, Rita Cucchiara
In this paper, we present a novel approach for NOC that learns to select the most relevant objects of an image, regardless of their adherence to the training set, and to constrain the generative process of a language model accordingly.
1 code implementation • 12 May 2021 • Roberto Bigazzi, Federico Landi, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
In this work, we detail how to transfer the knowledge acquired in simulation into the real world.
1 code implementation • 20 Apr 2021 • Samuele Poppi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
As the request for deep learning solutions increases, the need for explainability is even more fundamental.
1 code implementation • 15 Feb 2021 • Matteo Tomei, Lorenzo Baraldi, Simone Calderara, Simone Bronzin, Rita Cucchiara
The recently proposed action spotting task consists in finding the exact timestamp in which an event occurs.
Ranked #1 on Action Spotting on SoccerNet
no code implementations • 20 Jul 2020 • Matteo Fabbri, Fabio Lanzi, Riccardo Gasparini, Simone Calderara, Lorenzo Baraldi, Rita Cucchiara
In this document, we report our proposal for modeling the risk of possible contagiousity in a given area monitored by RGB cameras where people freely move and interact.
no code implementations • 14 Jul 2020 • Roberto Bigazzi, Federico Landi, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path.
1 code implementation • 1 Jul 2020 • Alessandro Simoni, Luca Bergamini, Andrea Palazzi, Simone Calderara, Rita Cucchiara
In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene.
1 code implementation • 26 May 2020 • Alessio Monti, Alessia Bertugli, Simone Calderara, Rita Cucchiara
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.
Ranked #1 on Trajectory Prediction on STATS SportVu NBA [ATK]
1 code implementation • 17 May 2020 • Alessia Bertugli, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.
no code implementations • 27 Apr 2020 • Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
The joint understanding of vision and language has been recently gaining a lot of attention in both the Computer Vision and Natural Language Processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering.
1 code implementation • CVPR 2020 • Matteo Fabbri, Fabio Lanzi, Simone Calderara, Stefano Alletto, Rita Cucchiara
At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation.
Ranked #6 on 3D Human Pose Estimation on Panoptic (using extra training data)
1 code implementation • CVPR 2020 • Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi
Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available.
Ranked #3 on Continual Learning on ImageNet-50 (5 tasks)
2 code implementations • CVPR 2020 • Marcella Cornia, Matteo Stefanini, Lorenzo Baraldi, Rita Cucchiara
Transformer-based architectures represent the state of the art in sequence modeling tasks like machine translation and language understanding.
Ranked #2 on Image Captioning on MS COCO
1 code implementation • 9 Dec 2019 • Matteo Tomei, Lorenzo Baraldi, Simone Calderara, Simone Bronzin, Rita Cucchiara
Action Detection is a complex task that aims to detect and classify human actions in video clips.
1 code implementation • 27 Nov 2019 • Federico Landi, Lorenzo Baraldi, Marcella Cornia, Massimiliano Corsini, Rita Cucchiara
Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination.
no code implementations • 7 Oct 2019 • Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
The ability to generate natural language explanations conditioned on the visual perception is a crucial step towards autonomous agents which can explain themselves and communicate with humans.
1 code implementation • 24 Jul 2019 • Andrea Palazzi, Luca Bergamini, Simone Calderara, Rita Cucchiara
An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.
1 code implementation • 5 Jul 2019 • Federico Landi, Lorenzo Baraldi, Massimiliano Corsini, Rita Cucchiara
In Vision-and-Language Navigation (VLN), an embodied agent needs to reach a target destination with the only guidance of a natural language instruction.
1 code implementation • 4 Mar 2019 • Stefano Pini, Marcella Cornia, Federico Bolelli, Lorenzo Baraldi, Rita Cucchiara
Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag.
no code implementations • 13 Feb 2019 • Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset.
no code implementations • 29 Jan 2019 • Federico Landi, Cees G. M. Snoek, Rita Cucchiara
This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos.
no code implementations • 23 Jan 2019 • Matteo Fabbri, Guido Borghi, Fabio Lanzi, Roberto Vezzani, Simone Calderara, Rita Cucchiara
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face?
1 code implementation • 23 Jan 2019 • Federico Fulgeri, Matteo Fabbri, Stefano Alletto, Simone Calderara, Rita Cucchiara
When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her.
1 code implementation • CVPR 2019 • Matteo Tomei, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain.
1 code implementation • CVPR 2019 • Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior.
1 code implementation • CVPR 2019 • Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
no code implementations • 12 Jun 2018 • Tewodros Mulugeta Dagnew, Dalia Coppi, Marcello Pelillo, Rita Cucchiara
Semi-supervised learning is a popular class of techniques to learn from labeled and unlabeled data.
1 code implementation • CVPR 2018 • Lorenzo Baraldi, Matthijs Douze, Rita Cucchiara, Hervé Jégou
This paper considers a learnable approach for comparing and aligning videos.
no code implementations • 30 May 2018 • Stefano Pini, Filippo Grazioli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
In this paper, an adversarial architecture for facial depth map estimation from monocular intensity images is presented.
2 code implementations • ECCV 2018 • Matteo Fabbri, Fabio Lanzi, Simone Calderara, Andrea Palazzi, Roberto Vezzani, Rita Cucchiara
Multi-People Tracking in an open-world setting requires a special effort in precise detection.
no code implementations • 12 Dec 2017 • Guido Borghi, Matteo Fabbri, Roberto Vezzani, Simone Calderara, Rita Cucchiara
Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only.
no code implementations • 21 Jul 2017 • Diego Ballotta, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images.
no code implementations • 7 Jul 2017 • Matteo Fabbri, Simone Calderara, Rita Cucchiara
In this paper we propose a deep architecture for detecting people attributes (e. g. gender, race, clothing ...) in surveillance contexts.
3 code implementations • 26 Jun 2017 • Andrea Palazzi, Guido Borghi, Davide Abati, Simone Calderara, Rita Cucchiara
Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies.
no code implementations • 26 Jun 2017 • Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations, and Recurrent Neural Networks to generate the corresponding captions.
Ranked #2 on Image Captioning on Flickr30k Captions test (using extra training data)
no code implementations • 1 Jun 2017 • Stefano Alletto, Davide Abati, Simone Calderara, Rita Cucchiara, Luca Rigazio
We address unsupervised optical flow estimation for ego-centric motion.
1 code implementation • 10 May 2017 • Andrea Palazzi, Davide Abati, Simone Calderara, Francesco Solera, Rita Cucchiara
In this work we aim to predict the driver's focus of attention.
no code implementations • 10 Mar 2017 • Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner.
no code implementations • 8 Mar 2017 • Guido Borghi, Roberto Vezzani, Rita Cucchiara
HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism.
no code implementations • 6 Mar 2017 • Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Recently, deep learning approaches have achieved promising results in various fields of computer vision.
no code implementations • CVPR 2017 • Guido Borghi, Marco Venturelli, Roberto Vezzani, Rita Cucchiara
In this work, we present a new deep learning framework for head localization and pose estimation on depth images.
2 code implementations • 29 Nov 2016 • Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations.
no code implementations • CVPR 2017 • Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description.
1 code implementation • 24 Nov 2016 • Andrea Palazzi, Francesco Solera, Simone Calderara, Stefano Alletto, Rita Cucchiara
Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task.
no code implementations • 5 Oct 2016 • Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
This paper presents a novel approach for temporal and semantic segmentation of edited videos into meaningful segments, from the point of view of the storytelling structure.
22 code implementations • 6 Sep 2016 • Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi
To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras observing more than 2, 700 identities over 85 minutes; and (iii) a reference software system as a comparison baseline.
2 code implementations • 5 Sep 2016 • Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps.
no code implementations • 28 Jul 2016 • Stefano Alletto, Giuseppe Serra, Rita Cucchiara
To effectively register an egocentric video sequence under these conditions, we propose to tackle the source of the problem: the matching process.
no code implementations • 9 Apr 2016 • Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
This paper presents a novel retrieval pipeline for video collections, which aims to retrieve the most significant parts of an edited video for a given query, and represent them with thumbnails which are at the same time semantically meaningful and aesthetically remarkable.
1 code implementation • 29 Oct 2015 • Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots.
no code implementations • ICCV 2015 • Francesco Solera, Simone Calderara, Rita Cucchiara
Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm.
no code implementations • 5 Aug 2015 • Francesco Solera, Simone Calderara, Rita Cucchiara
Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals.