41 citations · 148 across the 32 of their papers we have counts for
6 papers · 1 filter
Angular Supervision for Visual Object Grounding in Autoregressive Vision-Language Models
Parsa Esmaeilkhani, Longin Jan Latecki
Autoregressive vision-language models (VLMs) struggle to ground generated text in the correct image regions, and existing explainability methods, such as attention maps and Logit L…
Region-Aware CLS Token Augmentation for Fine-Grained Image Retrieval
Ian de Holanda Cavalcanti Bezerra, Vivek Trivedy, Lucas Pascotti Valem +1
Image retrieval methods often rely on a single global semantic descriptor extracted from an image, e.g., the [CLS] token in vision transformers. However, trying to squeeze all the…
ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
Ali Ansari, Yasmin Mohammadi, Farnoush Nili +3
Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiti…
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Qi Zhang, Fangping Lan, Cornelia Caragea +2
In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance wi…
Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention
Zhengkang Fan, Chengkun Sun, Russell Terry +2
Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing imaging modalities lack the nec…
Logit Lens Supervision for Patch-Level Explanations in Vision-Language Models
Parsa Esmaeilkhani, Longin Jan Latecki
Modern autoregressive Vision-Language Models (VLMs) can generate fluent answers while their visual-token representations become weakly tied to the image regions from which they ori…