Author name cluster

Lorenzo Baraldi

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4 papers

1 author row

NeurIPS Conference 2025 Conference Paper

vHector and HeisenVec: Scalable Vector Graphics Generation Through Large Language Models

Leonardo Zini
Elia Frigieri
Sebastiano Aloscari
Lorenzo Baraldi

We introduce HeisenVec, a large-scale dataset designed to advance research in vector graphics generation from natural language descriptions. Unlike conventional image generation datasets that focus on raster images, HeisenVec targets the structured and symbolic domain of Scalable Vector Graphics (SVG), where images are represented as sequences of drawing commands and style attributes. The dataset comprises 2. 2 million SVGs collected from different online sources, each paired with four complementary textual descriptions generated by multi-modal models. To ensure structural consistency and efficiency for autoregressive modeling, all SVGs are standardized through a pre-processing pipeline that unifies geometric primitives as paths, applies affine transformations, and compresses syntax via custom tokens set. HeisenVec exhibits broad coverage among visual styles and sequence lengths, with a substantial portion of samples exceeding 8, 000 tokens, making it particularly well-suited for benchmarking long-context language models. Our benchmark enables rigorous evaluation of text-conditioned SVG generation, encourages progress on sequence modeling with symbolic outputs, and bridges the gap between vision, graphics, and language. We release the dataset, tokenization tools, and evaluation pipeline to foster further research in this emerging domain.

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IS Journal 2024 Journal Article

Are Learnable Prompts the Right Way of Prompting? Adapting Vision-and-Language Models with Memory Optimization

Nicholas Moratelli
Manuele Barraco
Marcella Cornia
Lorenzo Baraldi
Rita Cucchiara

Few-shot learning (FSL) requires fine-tuning a pretrained model on a limited set of examples from novel classes. When applied to vision-and-language models, the dominant approach for FSL has been that of learning input prompts which can be concatenated to the input context of the model. Despite the considerable promise they hold, the effectiveness and expressive power of prompts are limited by the fact that they can only lie at the input of the architecture. In this article, we critically question the usage of learnable prompts, and instead leverage the concept of “implicit memory” to directly capture low- and high-level relationships within the attention mechanism at any layer of the architecture, thereby establishing an alternative to prompts in FSL. Our proposed approach, termed MemOp, exhibits superior performance across 11 widely recognized image classification datasets and a benchmark for contextual domain shift evaluation, effectively addressing the challenges associated with learnable prompts.

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IS Journal 2024 Journal Article

Multiclass Unlearning for Image Classification via Weight Filtering

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. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network’s components using memory matrices, enabling the network to demonstrate selective unlearning behavior for any class after training. By discovering weights that are specific to each class, our approach also recovers a representation of the classes which is explainable by design. We test the proposed framework on small- and medium-scale image classification datasets, with both convolution- and transformer-based backbones, showcasing the potential for explainable solutions through unlearning.

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NeurIPS Conference 2024 Conference Paper

Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments

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. This growth can be attributed to the recent availability of large navigation datasets in photo-realistic simulated environments, like Gibson and Matterport3D. However, the navigation tasks supported by these datasets are often restricted to the objects present in the environment at acquisition time. Also, they fail to account for the realistic scenario in which the target object is a user-specific instance that can be easily confused with similar objects and may be found in multiple locations within the environment. To address these limitations, we propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object by distinguishing it among multiple instances of the same category. The task is accompanied by PInNED, a dedicated new dataset composed of photo-realistic scenes augmented with additional 3D objects. In each episode, the target object is presented to the agent using two modalities: a set of visual reference images on a neutral background and manually annotated textual descriptions. Through comprehensive evaluations and analyses, we showcase the challenges of the PIN task as well as the performance and shortcomings of currently available methods designed for object-driven navigation, considering modular and end-to-end agents.

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