5 citations · 10 across the 17 of their papers we have counts for
8 papers · 1 filter
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models
Sri Harsha Dumpala, David Arps, Sageev Oore +2
Vision-language models (VLMs), serve as foundation models for multi-modal applications such as image captioning and text-to-image generation. Recent studies have highlighted limita…
Sensitivity of Generative VLMs to Semantically and Lexically Altered Prompts
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite the significant influx of prompt-tuning techniques for generative vision-language models (VLMs), it remains unclear how sensitive these models are to lexical and semantic a…
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression
Sri Harsha Dumpala, Katerina Dikaios, Abraham Nunes +3
Depression, a prevalent mental health disorder impacting millions globally, demands reliable assessment systems. Unlike previous studies that focus solely on either detecting depre…
Predicting Individual Depression Symptoms from Acoustic Features During Speech
Sebastian Rodriguez, Sri Harsha Dumpala, Katerina Dikaios +3
Current automatic depression detection systems provide predictions directly without relying on the individual symptoms/items of depression as denoted in the clinical depression rat…
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite their remarkable successes, state-of-the-art large language models (LLMs), including vision-and-language models (VLMs) and unimodal language models (ULMs), fail to understa…
An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
Scott C. Lowe, Joakim Bruslund Haurum, Sageev Oore +2
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embe…
VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Sastry +3
Despite their remarkable successes, state-of-the-art language models face challenges in grasping certain important semantic details. This paper introduces the VISLA (Variance and I…
Test-Time Training for Depression Detection
Sri Harsha Dumpala, Chandramouli Shama Sastry, Rudolf Uher +1
Previous works on depression detection use datasets collected in similar environments to train and test the models. In practice, however, the train and test distributions cannot be…