We address the challenge of representing long captions in vision-language models, such as CLIP. By design these models are limited by fixed, absolute positional encodings, restricting inputs to a maximum of 77 tokens and hindering performance on tasks requiring longer descriptions. Although recent work has attempted to overcome this limit, their proposed approaches struggle to model token relationships over longer distances and simply extend to a fixed new token length. Instead, we propose a generalizable method, named TULIP, able to upgrade the token length to any length for CLIP-like models. We do so by improving the architecture with relative position encodings, followed by a training procedure that (i) distills the original CLIP text encoder into an encoder with relative position encodings and (ii) enhances the model for aligning longer captions with images. By effectively encoding captions longer than the default 77 tokens, our model outperforms baselines on cross-modal tasks such as retrieval and text-to-image generation. The code repository is available at https://github.com/ivonajdenkoska/tulip.

Visual art understanding requires joint modeling of multiple perspectives and contextual inference rooted in cultural, historical, and stylistic knowledge. Recent multimodal large language models (MLLMs) demonstrate strong performance in generic captioning, primarily based on object recognition and training on large-scale generic data. They struggle in providing captions incorporating the multiple perspectives that fine art demands. In this work, we introduce ArtRAG, a novel training-free framework that integrates structured knowledge into a retrieval-augmented generation (RAG) pipeline for multi-perspective artwork explanation. ArtRAG automatically constructs an Art Context Knowledge Graph (ACKG) from domain-specific textual sources, organizing entities such as artists, themes, movements, and historical events into a rich, interpretable knowledge graph. At inference time, a multi-granular structured context retriever selects semantically and topologically relevant subgraphs to guide explanation generation. This approach enables MLLMs to produce contextually grounded, multi-perspective descriptions. Experiments on the SemArt and Artpedia datasets demonstrate that ArtRAG outperforms existing heavily trained baselines. Human evaluations further confirm ArtRAG's ability to generate coherent, informative, and culturally enriched interpretations of artworks.

Automating report generation for medical imaging promises to minimize labor and aid diagnosis in clinical practice. Deep learning algorithms have recently been shown to be capable of captioning natural photos. However, doing a similar thing for medical data, is difficult due to the variety in reports written by different radiologists with fluctuating levels of knowledge and experience. Current methods for automatic report generation tend to merely copy one of the training samples in the created report. To tackle this issue, we propose variational topic inference, a probabilistic approach for automatic chest X-ray report generation. Specifically, we introduce a probabilistic latent variable model where a latent variable defines a single topic. The topics are inferred in a conditional variational inference framework by aligning vision and language modalities in a latent space, with each topic governing the generation of one sentence in the report. We further adopt a visual attention module that enables the model to attend to different locations in the image while generating the descriptions. We conduct extensive experiments on two benchmarks, namely Indiana U. Chest X-rays and MIMIC-CXR. The results demonstrate that our proposed variational topic inference method can generate reports with novel sentence structure, rather than mere copies of reports used in training, while still achieving comparable performance to state-of-the-art methods in terms of standard language generation criteria.

An increasing number of real-world applications are using graph-structured datasets, imposing challenges to existing machine learning algorithms. Graph Convolutional Networks (GCNs) are deep learning models, specifically designed to operate on graphs. One of the most tedious steps in training GCNs is the choice of the hyperparameters, especially since they exhibit unique properties compared to other neural models. Not only machine learning beginners, but also experienced practitioners often have difficulties to properly tune their models. We hypothesize that having a tool that visualizes the effect of hyperparameters choice on the performance can accelerate the model development and improve the understanding of these black-box models. Additionally, observing clusters of certain nodes helps to empirically understand how a given prediction was made due to the feature propagation step of GCNs. Therefore, this demo introduces GCNIllustrator - a web-based visual analytics tool for illustrating the effect of hyperparameters on the predictions in a citations graph.