12 citations · 14 across the 14 of their papers we have counts for
16 papers
SCOPE:Planning for Hybrid Querying over Clinical Trial Data
Suparno Roy Chowdhury, Manan Roy Choudhury, Tejas Anvekar +5
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classificati…
FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use
Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury +5
Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema ex…
ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution
Yahia Alqurnawi, Preetom Biswas, Anmol Rao +3
Multimodal Large Language Models (mLLMs) are often used to answer questions in structured data such as tables in Markdown, JSON, and images. While these models can often give corre…
TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution
Tejas Anvekar, Junha Park, Rajat Jha +4
Question answering (QA) over structured tables requires not only accurate answers but also transparency about which cells support them. Existing table QA systems rarely provide fin…
DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Multimodal Large Language Models (MLLMs) can directly consume exam documents, threatening conventional assessments and academic integrity. We present DoPE (Decoy-Oriented Perturbat…
Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and c…
The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
Tejas Anvekar, Fenil Bardoliya, Pavan K. Turaga +2
Recent advances in multimodal large language models (MLLMs) have yielded increasingly powerful models, yet their perceptual capacities remain poorly characterized. In practice, mos…
TabReX : Tabular Referenceless eXplainable Evaluation
Tejas Anvekar, Junha Park, Aparna Garimella +1
Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely…
Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE
Abhijit Chakraborty, Chahana Dahal, Ashutosh Balasubramaniam +2
We revisit the efficacy of simple, real-valued embedding models for knowledge graph completion and introduce RelatE, an interpretable and modular method that efficiently integrates…
TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation
Vihang Pancholi, Jainit Bafna, Tejas Anvekar +2
Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies. To address t…
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective
Krishna Singh Rajput, Tejas Anvekar, Chitta Baral +1
Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large language models. While these approache…
Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations
Tejas Anvekar, Shivanand Venkanna Sheshappanavar
In this paper, we discuss Mahalanobis k-NN: A Statistical Lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted wit…
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View
Shivanand Venkanna Sheshappanavar, Tejas Anvekar, Shivanand Kundargi +2
Fine-grained grocery object recognition is an important computer vision problem with broad applications in automatic checkout, in-store robotic navigation, and assistive technologi…
PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark
Shivanand Kundargi, Tejas Anvekar, Ramesh Ashok Tabib +1
Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class in…
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning
Tejas Anvekar, Dena Bazazian
In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role. However, it poses an arduous challenge due to the sparsity, irregularity, and…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…