arXiv:2610.18047v1 Announce Type: new
Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore thei...
By Slimane Larabi
The paper proposes a sketch-based visual grounding framework that converts text embeddings into topographic sketches to enhance visualization, explainability, and information retrieval. It evaluates the method on the MS-COCO dataset, finding that while SBERT outperforms the sketch-based approach in top‑1 retrieval, the two methods achieve comparable top‑5 results, and a cascaded combination improves performance further. The study suggests that sketch-based representations could be valuable for retrieval tasks if trained directly on text.
arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.
By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal
Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care.
arXiv:2507. 09092v2 Announce Type: replace-cross Abstract: With the intervention of machine vision in our crucial day to day necessities including healthcare and automated power plants, attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network provides specific inferences.
By Ram S Iyer
arXiv:2608. 19407v1 Announce Type: cross Abstract: Deep Learning models can include billions of parameters or more, making it difficult to explain their internal transformations and outputs.
By Manasi Nerurkar, Ali A. Minai