5 citations · 6 across the 9 of their papers we have counts for
11 papers
To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech
Debajyoti Mazumder, Mamta, Abhirama Subramanyam Penamakuri
Online misinformation increasingly appears in spoken formats such as news clips, podcasts, interviews, political speeches, and social media videos, creating a need for fact-checkin…
RA-CoA: Training-free Fashion Image Captioning via Retrieval-Augmented Chain-of-Attributes
Abhirama Subramanyam Penamakuri, Shreya Shukla, Anand Mishra
Fashion Image Captioning (FIC) plays a vital role in enhancing user experience and product search in e-commerce platforms. Unlike natural scene image captioning, FIC requires fine-…
PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis
K Lokesh, Abhirama Subramanyam Penamakuri, Uday Agarwal +4
Traditionally, AI research in medical diagnosis has largely centered on image analysis. While this has led to notable advancements, the absence of patient-reported symptoms continu…
Bharat Scene Text: A Novel Comprehensive Dataset and Benchmark for Indian Language Scene Text Understanding
Anik De, Abhirama Subramanyam Penamakuri, Rajeev Yadav +6
Reading scene text, that is, text appearing in images, has numerous application areas, including assistive technology, search, and e-commerce. Although scene text recognition in En…
When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs
Abhirama Subramanyam Penamakuri, Navlika Singh, Piyush Arora +1
Large Vision-Language Models (L-VLMs) have demonstrated remarkable performance in various vision and language tasks, including visual question answering (VQA). However, their high…
Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs
Somraj Gautam, Abhirama Subramanyam Penamakuri, Abhishek Bhandari +1
We introduce MMCRICBENCH-3K, a benchmark for Visual Question Answering (VQA) on cricket scorecards, designed to evaluate large vision-language models (LVLMs) on complex numerical a…
Audiopedia: Audio QA with Knowledge
Abhirama Subramanyam Penamakuri, Kiran Chhatre, Akshat Jain
In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlik…
Visual Text Matters: Improving Text-KVQA with Visual Text Entity Knowledge-aware Large Multimodal Assistant
Abhirama Subramanyam Penamakuri, Anand Mishra
We revisit knowledge-aware text-based visual question answering, also known as Text-KVQA, in the light of modern advancements in large multimodal models (LMMs), and make the follow…
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
Abhirama Subramanyam Penamakuri, Manish Gupta, Mithun Das Gupta +1
We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must…
Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification
Nakul Sharma, Abhirama S. Penamakuri, Anand Mishra
In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging…
COFAR: Commonsense and Factual Reasoning in Image Search
Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia +3
One characteristic that makes humans superior to modern artificially intelligent models is the ability to interpret images beyond what is visually apparent. Consider the following…