Search Results for author: Rakesh Ranjan

Found 30 papers, 8 papers with code

Enhancing 2D Representation Learning with a 3D Prior

no code implementations4 Jun 2024 Mehmet Aygün, Prithviraj Dhar, Zhicheng Yan, Oisin Mac Aodha, Rakesh Ranjan

Self-supervised learning attempts to circumvent the requirement for labeled data by learning representations from raw unlabeled visual data alone.

Representation Learning Self-Supervised Learning

Sharing Key Semantics in Transformer Makes Efficient Image Restoration

no code implementations30 May 2024 Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Ming-Hsuan Yang, Nicu Sebe

Additionally, for IR, it is commonly noted that small segments of a degraded image, particularly those closely aligned semantically, provide particularly relevant information to aid in the restoration process, as they contribute essential contextual cues crucial for accurate reconstruction.

Image Restoration

UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model

1 code implementation CVPR 2024 Shuai Yuan, Lei Luo, Zhuo Hui, Can Pu, Xiaoyu Xiang, Rakesh Ranjan, Denis Demandolx

Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information.

Object Optical Flow Estimation

CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians

no code implementations28 Mar 2024 Avinash Paliwal, Wei Ye, Jinhui Xiong, Dmytro Kotovenko, Rakesh Ranjan, Vikas Chandra, Nima Khademi Kalantari

The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian Splatting (3DGS).

3D Reconstruction Decoder +1

Towards Image Ambient Lighting Normalization

no code implementations27 Mar 2024 Florin-Alexandru Vasluianu, Tim Seizinger, Zongwei Wu, Rakesh Ranjan, Radu Timofte

However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.

Benchmarking Image Restoration +1

Garment3DGen: 3D Garment Stylization and Texture Generation

no code implementations27 Mar 2024 Nikolaos Sarafianos, Tuur Stuyck, Xiaoyu Xiang, Yilei Li, Jovan Popovic, Rakesh Ranjan

We present a plethora of quantitative and qualitative comparisons on various assets both real and generated and provide use-cases of how one can generate simulation-ready 3D garments.

Image to 3D Texture Synthesis

MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction

no code implementations20 Feb 2024 Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, Rakesh Ranjan

MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.

3D Object Reconstruction 3D Reconstruction +2

Key-Graph Transformer for Image Restoration

no code implementations4 Feb 2024 Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Nicu Sebe

While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution.

Graph Attention Image Restoration

Taming Mode Collapse in Score Distillation for Text-to-3D Generation

no code implementations CVPR 2024 Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra

In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.

3D Generation Prompt Engineering +1

Training Neural Networks on RAW and HDR Images for Restoration Tasks

1 code implementation6 Dec 2023 Lei Luo, ALEXANDRE CHAPIRO, Xiaoyu Xiang, Yuchen Fan, Rakesh Ranjan, Rafal Mantiuk

Our results indicate that neural networks train significantly better on HDR and RAW images represented in display-encoded color spaces, which offer better perceptual uniformity than linear spaces.

Deblurring Denoising +2

MoVideo: Motion-Aware Video Generation with Diffusion Models

no code implementations19 Nov 2023 Jingyun Liang, Yuchen Fan, Kai Zhang, Radu Timofte, Luc van Gool, Rakesh Ranjan

While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos and images, i. e., motion.

Image Generation Image to Video Generation +1

NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation

no code implementations29 Aug 2023 Tim Meinhardt, Matt Feiszli, Yuchen Fan, Laura Leal-Taixe, Rakesh Ranjan

Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing.

Ranked #5 on Video Instance Segmentation on YouTube-VIS validation (using extra training data)

Instance Segmentation Segmentation +2

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

no code implementations CVPR 2023 Hyunyoung Jung, Zhuo Hui, Lei Luo, Haitao Yang, Feng Liu, Sungjoo Yoo, Rakesh Ranjan, Denis Demandolx

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs.

Optical Flow Estimation

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

1 code implementation CVPR 2023 Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, Luc van Gool

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.

Image Deblurring Image Defocus Deblurring +1

FSID: Fully Synthetic Image Denoising via Procedural Scene Generation

1 code implementation7 Dec 2022 Gyeongmin Choe, Beibei Du, Seonghyeon Nam, Xiaoyu Xiang, Bo Zhu, Rakesh Ranjan

To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks.

Image Denoising Scene Generation +1

Fast Point Cloud Generation with Straight Flows

1 code implementation CVPR 2023 Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu

We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods.

Point Cloud Completion

Consistent Direct Time-of-Flight Video Depth Super-Resolution

1 code implementation CVPR 2023 Zhanghao Sun, Wei Ye, Jinhui Xiong, Gyeongmin Choe, Jialiang Wang, Shuochen Su, Rakesh Ranjan

We believe the methods and dataset are beneficial to a broad community as dToF depth sensing is becoming mainstream on mobile devices.

Super-Resolution

HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars

no code implementations28 Mar 2022 Xiaoyu Xiang, Jon Morton, Fitsum A Reda, Lucas Young, Federico Perazzi, Rakesh Ranjan, Amit Kumar, Andrea Colaco, Jan Allebach

Compared with previous methods, our network can effectively handle the misalignment between the input and the reference without requiring facial priors and learn the aggregated reference set representation in an end-to-end manner.

Image Super-Resolution

Learning Spatio-Temporal Downsampling for Effective Video Upscaling

no code implementations15 Mar 2022 Xiaoyu Xiang, Yapeng Tian, Vijay Rengarajan, Lucas Young, Bo Zhu, Rakesh Ranjan

Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts.

Quantization

VRT: A Video Restoration Transformer

1 code implementation28 Jan 2022 Jingyun Liang, JieZhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc van Gool

Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.

Deblurring Denoising +7

EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

no code implementations CVPR 2022 Prithviraj Dhar, Amit Kumar, Kirsten Kaplan, Khushi Gupta, Rakesh Ranjan, Rama Chellappa

To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting.

Feature-Align Network with Knowledge Distillation for Efficient Denoising

no code implementations2 Mar 2021 Lucas D. Young, Fitsum A. Reda, Rakesh Ranjan, Jon Morton, Jun Hu, Yazhu Ling, Xiaoyu Xiang, David Liu, Vikas Chandra

(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.

Decoder Efficient Neural Network +3

EVRNet: Efficient Video Restoration on Edge Devices

no code implementations3 Dec 2020 Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra

Video transmission applications (e. g., conferencing) are gaining momentum, especially in times of global health pandemic.

Denoising SSIM +2

Cannot find the paper you are looking for? You can Submit a new open access paper.