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Anand Mishra

13 accepted papers

2026

PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis

K Lokesh, Abhirama Subramanyam Penamakuri, Uday Agarwal, Apoorva Challa, Shreya K Gowda, Somesh Gupta +1
AAAI 2026technical

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 continues to hinder diagnostic accuracy. To address this, we propose a Pre-Consultation Dialogue Framework (PCDF) that mimics r

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2025

PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures

Shreya Shukla, Nakul Sharma, Manish Gupta, Anand Mishra
AAAI 2025technical

Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial to effective knowledge sharing and enabling the replication and protection of intellectual property. However, automation of this task has been largely overlooked by the research community. To this en…

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2025

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, Anand Mishra
EMNLP 2025

Large Vision-Language Models (L-VLMs) have demonstrated remarkable performance in various vision and language tasks, including Visual Question Answering (VQA). However, their high computational cost makes them impractical for resource-constrained settings and inference-heavy applications. In contras

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2024

Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions

Prajwal Gatti, Kshitij Parikh, Dhriti Prasanna Paul, Manish Gupta, Anand Mishra
AAAI 2024technical

Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for ‘numbats.’ Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., “numbat digging in…

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2024

QDETRv: Query-Guided DETR for One-Shot Object Localization in Videos

Yogesh Kumar, Saswat Mallick, Anand Mishra, Sowmya Rasipuram, Anutosh Maitra, Roshni Ramnani
AAAI 2024technical

In this work, we study one-shot video object localization problem that aims to localize instances of unseen objects in the target video using a single query image of the object. Toward addressing this challenging problem, we extend a popular and successful object detection method, namely DETR (Detec…

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2024

Visual Text Matters: Improving Text-KVQA with Visual Text Entity Knowledge-aware Large Multimodal Assistant

Abhirama Subramanyam Penamakuri, Anand Mishra
EMNLP 2024main

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 following contributions: (i) We propose VisTEL – a principled approach to perform visual text entity linking. The proposed VisT…

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2023

Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering

Abhirama Subramanyam Penamakuri, Manish Gupta, Mithun Das Gupta, Anand Mishra
IJCAI 2023poster

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 first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this probl…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2019

From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason

Ajeet Kumar Singh, Anand Mishra, Shashank Shekhar, Anirban Chakraborty
ICCV 2019oral

Text present in images are not merely strings, they provide useful cues about the image. Despite their utility in better image understanding, scene texts are not used in traditional visual question answering (VQA) models. In this work, we present a VQA model which can read scene texts and perform re…

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