12 citations · 33 across the 15 of their papers we have counts for
17 papers
Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding
Qitong Wang, Fan Du, Pranav Maneriker +2
The rapid rise of Vision-Language Models (VLMs) in egocentric visual understanding has made low-latency inference in human-robot collaborative (HRC) tasks increasingly critical. We…
APT: Atomic Physical Transitions for Causal Video-Language Understanding
Shang Wu, Haoran Lu, Songling Liu +6
Physical events are not understood by their names alone, but by the causal state changes that compose them. A clip-level label such as "bounce" can be correct while hiding the proc…
PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation
Shang Wu, Chenwei Xu, Zhuofan Xia +6
State-of-the-art text-to-video (T2V) generators frequently violate physical laws despite high visual quality. We show this stems from insufficient physical constraints in prompts r…
Towards Sparse Video Understanding and Reasoning
Chenwei Xu, Zhen Ye, Shang Wu +8
We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA). Instead of uniformly sampling frames…
DPAR: Dynamic Patchification for Efficient Autoregressive Visual Generation
Divyansh Srivastava, Akshay Mehra, Pranav Maneriker +5
Decoder-only autoregressive image generation typically relies on fixed-length tokenization schemes whose token counts grow quadratically with resolution, substantially increasing t…
A Generic Framework for Conformal Fairness
Aditya T. Vadlamani, Anutam Srinivasan, Pranav Maneriker +2
Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding th…
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
Yuntian He, Pranav Maneriker, Anutam Srinivasan +2
Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural n…
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan +3
Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has bu…
URLTran: Improving Phishing URL Detection Using Transformers
Pranav Maneriker, Jack W. Stokes, Edir Garcia Lazo +3
Browsers often include security features to detect phishing web pages. In the past, some browsers evaluated an unknown URL for inclusion in a list of known phishing pages. However,…
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Pranav Maneriker, Yuntian He, Srinivasan Parthasarathy
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host the…
DrugDBEmbed : Semantic Queries on Relational Database using Supervised Column Encodings
Bortik Bandyopadhyay, Pranav Maneriker, Vedang Patel +3
Traditional relational databases contain a lot of latent semantic information that have largely remained untapped due to the difficulty involved in automatically extracting such in…
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing
Goonmeet Bajaj, Bortik Bandyopadhyay, Daniel Schmidt +3
Visual Question Answering (VQA) systems are tasked with answering natural language questions corresponding to a presented image. Traditional VQA datasets typically contain question…
LEAF-QA: Locate, Encode & Attend for Figure Question Answering
Ritwick Chaudhry, Sumit Shekhar, Utkarsh Gupta +3
We introduce LEAF-QA, a comprehensive dataset of densely annotated figures/charts, constructed from real-world open data sources, along with ~2 million question-answer (Q…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…
Towards Open Intent Discovery for Conversational Text
Nikhita Vedula, Nedim Lipka, Pranav Maneriker +1
Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model…
Forecasting Granular Audience Size for Online Advertising
Ritwik Sinha, Dhruv Singal, Pranav Maneriker +4
Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by th…
Generating Appealing Brand Names
Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani
Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding…