74 citations · 222 across the 29 of their papers we have counts for
30 papers
Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?
Apratim Bhattacharyya, Shweta Mahajan, Sanjay Haresh +5
Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large lang…
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9
Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional im…
Generative Scenario Rollouts for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Shizhong Han +10
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…
DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos
Rajeev Yasarla, Shizhong Han, Hong Cai +1
Camera-based 3D object detection in Bird's Eye View (BEV) is one of the most important perception tasks in autonomous driving. Earlier methods rely on dense BEV features, which are…
RoCA: Robust Cross-Domain End-to-End Autonomous Driving
Rajeev Yasarla, Shizhong Han, Hsin-Pai Cheng +7
End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant potential. However, few studies have looked into the practical challenge of deploym…
Distilling Multi-modal Large Language Models for Autonomous Driving
Deepti Hegde, Rajeev Yasarla, Hong Cai +7
Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as…
ToSA: Token Selective Attention for Efficient Vision Transformers
Manish Kumar Singh, Rajeev Yasarla, Hong Cai +2
In this paper, we propose a novel token selective attention approach, ToSA, which can identify tokens that need to be attended as well as those that can skip a transformer layer. M…
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai +6
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimati…
HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation
Antoine Mercier, Ramin Nakhli, Mahesh Reddy +4
Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in…
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Rajeev Yasarla, Hong Cai, Jisoo Jeong +3
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video de…
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images
Nithin Gopalakrishnan Nair, Rajeev Yasarla, Vishal M. Patel
In recent years, deep neural network-based restoration methods have achieved state-of-the-art results in various image deblurring tasks. However, one major drawback of deep learnin…
Learning to restore images degraded by atmospheric turbulence using uncertainty
Rajeev Yasarla, Vishal M. Patel
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index o…
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather…
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images
Rajeev Yasarla, Carey E. Priebe, Vishal Patel
In recent years, convolutional neural network-based single image adverse weather removal methods have achieved significant performance improvements on many benchmark datasets. Howe…
3SD: Self-Supervised Saliency Detection With No Labels
Rajeev Yasarla, Renliang Weng, Wongun Choi +2
We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training. The generated pseudo-GT label…
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
Jeya Maria Jose Valanarasu, Rajeev Yasarla, Vishal M. Patel
Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to…
Network Architecture Search for Face Enhancement
Rajeev Yasarla, Hamid Reza Vaezi Joze, Vishal M Patel
Various factors such as ambient lighting conditions, noise, motion blur, etc. affect the quality of captured face images. Poor quality face images often reduce the performance of f…
Exploring Overcomplete Representations for Single Image Deraining using CNNs
Rajeev Yasarla, Jeya Maria Jose Valanarasu, Vishal M. Patel
Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. M…
Semi-Supervised Image Deraining using Gaussian Processes
Rajeev Yasarla, V. A. Sindagi, V. M. Patel
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in t…
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas
Pengfei Guo, Puyang Wang, Rajeev Yasarla +3
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard a…
Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs
Rajeev Yasarla, Vishal M Patel
Atmospheric turbulence significantly affects imaging systems which use light that has propagated through long atmospheric paths. Images captured under such condition suffer from a…
Learning to Count in the Crowd from Limited Labeled Data
Vishwanath A. Sindagi, Rajeev Yasarla, Deepak Sam Babu +2
Recent crowd counting approaches have achieved excellent performance. However, they are essentially based on fully supervised paradigm and require large number of annotated samples…
Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
Rajeev Yasarla, Vishwanath A. Sindagi, Vishal M. Patel
Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in t…
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
Due to its variety of applications in the real-world, the task of single image-based crowd counting has received a lot of interest in the recent years. Recently, several approaches…