Search Results for author:
Found 23 papers, 9 papers with code
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
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 estimation by making it learn to predict the future at training.
Ranked #2 on
Monocular Depth Estimation
on KITTI Eigen split
HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation
Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task.
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #12 on
Monocular Depth Estimation
on KITTI Eigen split
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images
This results in a pair of images with colored noise.
Learning to restore images degraded by atmospheric turbulence using uncertainty
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 of refraction of the atmosphere.
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training.
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images
Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images.
3SD: Self-Supervised Saliency Detection With No Labels
Our method generates and uses pseudo-ground truth labels for training.
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand.
Ranked #1 on
Single Image Deraining
on Raindrop
Network Architecture Search for Face Enhancement
Poor quality face images often reduce the performance of face analysis and recognition systems.
Exploring Overcomplete Representations for Single Image Deraining using CNNs
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.
Semi-Supervised Image Deraining using Gaussian Processes
We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images.
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images.
Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs
Atmospheric turbulence significantly affects imaging systems which use light that has propagated through long atmospheric paths.
Learning to Count in the Crowd from Limited Labeled Data
In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data.
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes
Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain100H and DDN-SIRR), we show that the proposed method, when trained on limited labeled data, achieves on-par performance with fully-labeled training.
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
We show that our method can synthesize high-quality US images for every manipulated segmentation label with qualitative and quantitative improvements over the recent state-of-the-art synthesis methods.
Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images.
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.
Confidence Measure Guided Single Image De-raining
Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density.
Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring.
Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining
Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image.
Ranked #8 on
Single Image Deraining
on Test2800
