5 citations · 6 across the 9 of their papers we have counts for
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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…
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…