30 citations · 66 across the 10 of their papers we have counts for
12 papers
Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning
Vishwas Sathish, Viresh Ranjan, Xinliang Zhu +2
Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms such as GRPO have improved long…
Rethinking Visual Information Processing in Multimodal LLMs
Dongwan Kim, Viresh Ranjan, Takashi Nagata +2
Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherently struggles to effectively integrate visual features due to the inherent mis…
Interactive Class-Agnostic Object Counting
Yifeng Huang, Viresh Ranjan, Minh Hoai
We propose a novel framework for interactive class-agnostic object counting, where a human user can interactively provide feedback to improve the accuracy of a counter. Our framewo…
Zero-shot Object Counting
Jingyi Xu, Hieu Le, Vu Nguyen +2
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods re…
Exemplar Free Class Agnostic Counting
Viresh Ranjan, Minh Hoai
We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category. Al…
Learning To Count Everything
Viresh Ranjan, Udbhav Sharma, Thu Nguyen +1
Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, t…
Uncertainty Estimation and Sample Selection for Crowd Counting
Viresh Ranjan, Boyu Wang, Mubarak Shah +1
We present a method for image-based crowd counting, one that can predict a crowd density map together with the uncertainty values pertaining to the predicted density map. To obtain…
A Study of Human Gaze Behavior During Visual Crowd Counting
Raji Annadi, Yupei Chen, Viresh Ranjan +3
In this paper, we describe our study on how humans allocate their attention during visual crowd counting. Using an eye tracker, we collect gaze behavior of human participants who a…
Crowd Transformer Network
Viresh Ranjan, Mubarak Shah, Minh Hoai Nguyen
In this paper, we tackle the problem of Crowd Counting, and present a crowd density estimation based approach for obtaining the crowd count. Most of the existing crowd counting app…
Fake Sentence Detection as a Training Task for Sentence Encoding
Viresh Ranjan, Heeyoung Kwon, Niranjan Balasubramanian +1
Sentence encoders are typically trained on language modeling tasks with large unlabeled datasets. While these encoders achieve state-of-the-art results on many sentence-level tasks…
Iterative Crowd Counting
Viresh Ranjan, Hieu Le, Minh Hoai
In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting…
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Stefan Lee, Senthil Purushwalkam, Michael Cogswell +3
Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutio…