640 citations · 3.8k across the 145 of their papers we have counts for
163 papers
Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos
Jay N. Paranjape, Shameema Sikder, Vishal M. Patel +1
Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a pa…
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang +8
Harnessing the power of pre-training on large-scale datasets like ImageNet forms a fundamental building block for the progress of representation learning-driven solutions in comput…
GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
Nisarg A. Shah, Shameema Sikder, S. Swaroop Vedula +1
Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for s…
Securing Deep Generative Models with Universal Adversarial Signature
Yu Zeng, Mo Zhou, Yuan Xue +1
Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to…
T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities
Kangfu Mei, Mo Zhou, Vishal M. Patel
Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces. While DPF shows great potential for unifying data generation of vari…
Analyzing Bias in Diffusion-based Face Generation Models
Malsha V. Perera, Vishal M. Patel
Diffusion models are becoming increasingly popular in synthetic data generation and image editing applications. However, these models can amplify existing biases and propagate them…
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
Deepti Hegde, Jeya Maria Jose Valanarasu, Vishal M. Patel
Vision-Language models like CLIP have been widely adopted for various tasks due to their impressive zero-shot capabilities. However, CLIP is not suitable for extracting 3D geometri…
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Vibashan VS, Ning Yu, Chen Xing +5
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati +2
Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target da…
ReBotNet: Fast Real-time Video Enhancement
Jeya Maria Jose Valanarasu, Rahul Garg, Andeep Toor +5
Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework t…
Deep Metric Learning for Unsupervised Remote Sensing Change Detection
Wele Gedara Chaminda Bandara, Vishal M. Patel
Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cove…
: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara +1
Crowd counting is a fundamental problem in crowd analysis which is typically accomplished by estimating a crowd density map and summing over the density values. However, this appro…
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Huali Xu, Shuaifeng Zhi, Shuzhou Sun +2
While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this,…
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel
Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentime…
Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consistin…
VIDM: Video Implicit Diffusion Models
Kangfu Mei, Vishal M. Patel
Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on…
SceneComposer: Any-Level Semantic Image Synthesis
Yu Zeng, Zhe Lin, Jianming Zhang +4
We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More s…
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami +3
Masked Autoencoders (MAEs) learn generalizable representations for image, text, audio, video, etc., by reconstructing masked input data from tokens of the visible data. Current MAE…
Open-Set Automatic Target Recognition
Bardia Safaei, Vibashan VS, Celso M. de Melo +2
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are exten…
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…
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. A…
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Vishal M. Patel
Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using…
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation
Shao-Yuan Lo, Wei Wang, Jim Thomas +3
Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supe…
Deep Semantic Statistics Matching (D2SM) Denoising Network
Kangfu Mei, Vishal M. Patel, Rui Huang
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learnin…