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Neel Joshi

10 accepted papers

2025

Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models

Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran, Safoora Yousefi, Thomas FEL, Soheil Feizi +3
ICLR 2025poster

With models getting stronger, evaluations have grown more complex, testing multiple skills in one benchmark and even in the same instance at once. However, skill-wise performance is obscured when inspecting aggregate accuracy, under-utilizing the rich signal modern benchmarks contain. We propose an…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models

Jiayu Wang, Yifei Ming, Zhenmei Shi, Vibhav Vineet, Xin Wang, Yixuan Li +1
NeurIPS 2024poster

Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance across a wide range of tasks and domains. Despite this promise, spatial understanding and reasoning—a fundamental component of human cognition—remains under-explored. We propose SpatialEval, a nov…

Cited by 34Source ↗PDF ↗code ↗Scholar ↗
2023

Exploring the Sim2Real Gap Using Digital Twins

Sruthi Sudhakar, Jon Hanzelka, Josh Bobillot, Tanmay Randhavane, Neel Joshi, Vibhav Vineet
ICCV 2023poster

It is very time consuming to create datasets for training computer vision models. An emerging alternative is to use synthetic data, but if the synthetic data is not similar enough to the real data, the performance is typically below that of training with real data. Thus using synthetic data still re…

Cited by 5PDF ↗code ↗Scholar ↗
2023

HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World

Xin Wang, Taein Kwon, Mahdi Rad, Bowen Pan, Ishani Chakraborty, Sean Andrist +6
ICCV 2023poster

Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community. This work is part of a broader research effort to develop intelligent agents that can interactively guide humans through performing task…

Cited by 55PDF ↗code ↗Scholar ↗
2022

Neural-Sim: Learning to Generate Training Data with NeRF

Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song +3
ECCV 2022poster

"Traditional approaches for training a computer vision models requires collecting and labelling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredibly time-consuming, and it is challenging to ensure that the captured data distribution maps well…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2022

Robust Contrastive Learning Against Noisy Views

Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba +2
CVPR 2022poster

Contrastive learning relies on an assumption that positive pairs contain related views that share certain underlying information about an instance, e.g., patches of an image or co-occurring multimodal signals of a video. What if this assumption is violated? The literature suggests that contrastive l…

Cited by 100PDF ↗code ↗Scholar ↗
2022

Visual Attention Emerges from Recurrent Sparse Reconstruction

Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang
ICML 2022spotlight

Visual attention helps achieve robust perception under noise, corruption, and distribution shifts in human vision, which are areas where modern neural networks still fall short. We present VARS, Visual Attention from Recurrent Sparse reconstruction, a new attention formulation built on two prominent…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2018

Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility

Benjamin Hepp, Debadeepta Dey, Sudipta N. Sinha, Ashish Kapoor, Neel Joshi, Otmar Hilliges
ECCV 2018poster

Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predic…

Cited by 62Source ↗PDF ↗Scholar ↗
2017

Personalized Cinemagraphs Using Semantic Understanding and Collaborative Learning

Tae-Hyun Oh, Kyungdon Joo, Neel Joshi, Baoyuan Wang, In So Kweon, Sing Bing Kang
ICCV 2017poster

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way an…

Cited by 19PDF ↗Scholar ↗
2017

Submodular Trajectory Optimization for Aerial 3D Scanning

Mike Roberts, Debadeepta Dey, Anh Truong, Sudipta Sinha, Shital Shah, Ashish Kapoor +2
ICCV 2017poster

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate…

Cited by 177PDF ↗Scholar ↗