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Found 42 papers, 24 papers with code
Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form.
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
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.
Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images
Creating high-quality and realistic images is now possible thanks to the impressive advancements in image generation.
Trends, Applications, and Challenges in Human Attention Modelling
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.
The (R)Evolution of Multimodal Large Language Models: A Survey
Connecting text and visual modalities plays an essential role in generative intelligence.
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
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.
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements.
With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning
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.
Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training
The use of self-supervised pre-training has emerged as a promising approach to enhance the performance of visual tasks such as image classification.
LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
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.
Multi-Class Explainable Unlearning for Image Classification via Weight Filtering
Machine Unlearning has recently been emerging as a paradigm for selectively removing the impact of training datapoints from a network.
Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing
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.
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Recent advancements in diffusion models have enabled the generation of realistic deepfakes by writing textual prompts in natural language.
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation
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.
Embodied Agents for Efficient Exploration and Smart Scene Description
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.
Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions
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.
The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition
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.
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
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
Retrieval-Augmented Transformer for Image Captioning
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.
Embodied Navigation at the Art Gallery
This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information.
Spot the Difference: A Novel Task for Embodied Agents in Changing Environments
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.
Dress Code: High-Resolution Multi-Category Virtual Try-On
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
CaMEL: Mean Teacher Learning for Image Captioning
Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities.
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets
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.
Focus on Impact: Indoor Exploration with Intrinsic Motivation
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.
Working Memory Connections for LSTM
Numerical results suggest that the cell state contains useful information that is worth including in the gate structure.
From Show to Tell: A Survey on Deep Learning-based Image Captioning
Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation.
Learning to Select: A Fully Attentive Approach for Novel Object Captioning
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.
Out of the Box: Embodied Navigation in the Real World
In this work, we detail how to transfer the knowledge acquired in simulation into the real world.
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis
As the request for deep learning solutions increases, the need for explainability is even more fundamental.
Explore and Explain: Self-supervised Navigation and Recounting
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.
A Novel Attention-based Aggregation Function to Combine Vision and Language
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.
Meshed-Memory Transformer for Image Captioning
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
Multimodal Attention Networks for Low-Level Vision-and-Language Navigation
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.
SMArT: Training Shallow Memory-aware Transformers for Robotic Explainability
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.
Artpedia
As vision and language techniques are widely applied to realistic images , there is a growing interest in designing visual-semantic models suitable for more complex and challenging scenarios.
M-VAD Names: a Dataset for Video Captioning with Naming
Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions
Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior.
Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation
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.
Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention
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)
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations.
A Deep Multi-Level Network for Saliency Prediction
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.
