pwc-archive/papers-with-abstracts huggingface dataset:
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
Authors: Yuqi Han, Tao Yu, Xiaohang Yu, Yuwang Wang, Qionghai Dai
Date: 2023-04-26 00:00:00
Tasks: Image Super-Resolution, NeRF, Super-Resolution

The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on the make full use of the image resolution to generate novel views, but less considering the generation of details under the limited input resolution. In analogy to the extensive usage of image super-resolution, NeRF super-resolution is an effective way to generate the high-resolution implicit representation of 3D scenes and holds great potential applications. Up to now, such an important topic is still under-explored. In this paper, we propose a NeRF super-resolution method, named Super-NeRF, to generate high-resolution NeRF from only low-resolution inputs. Given multi-view low-resolution images, Super-NeRF constructs a consistency-controlling super-resolution module to generate view-consistent high-resolution details for NeRF. Specifically, an optimizable latent code is introduced for each low-resolution input image to control the 2D super-resolution images to converge to the view-consistent output. The latent codes of each low-resolution image are optimized synergistically with the target Super-NeRF representation to fully utilize the view consistency constraint inherent in NeRF construction. We verify the effectiveness of Super-NeRF on synthetic, real-world, and AI-generated NeRF datasets. Super-NeRF achieves state-of-the-art NeRF super-resolution performance on high-resolution detail generation and cross-view consistency.
EasyPortrait -- Face Parsing and Portrait Segmentation Dataset
Authors: Karina Kvanchiani, Elizaveta Petrova, Karen Efremyan, Alexander Sautin, Alexander Kapitanov
Date: 2023-04-26 00:00:00
Tasks: Diversity, Domain Generalization, Face Parsing, Image Segmentation, Portrait Segmentation, Segmentation, Semantic Segmentation

Recently, video conferencing apps have become functional by accomplishing such computer vision-based features as real-time background removal and face beautification. Limited variability in existing portrait segmentation and face parsing datasets, including head poses, ethnicity, scenes, and occlusions specific to video conferencing, motivated us to create a new dataset, EasyPortrait, for these tasks simultaneously. It contains 40,000 primarily indoor photos repeating video meeting scenarios with 13,705 unique users and fine-grained segmentation masks separated into 9 classes. Inappropriate annotation masks from other datasets caused a revision of annotator guidelines, resulting in EasyPortrait's ability to process cases, such as teeth whitening and skin smoothing. The pipeline for data mining and high-quality mask annotation via crowdsourcing is also proposed in this paper. In the ablation study experiments, we proved the importance of data quantity and diversity in head poses in our dataset for the effective learning of the model. The cross-dataset evaluation experiments confirmed the best domain generalization ability among portrait segmentation datasets. Moreover, we demonstrate the simplicity of training segmentation models on EasyPortrait without extra training tricks. The proposed dataset and trained models are publicly available.
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
Authors: Xiaoqing Liu, Kenji Ono, Ryoma Bise
Date: 2023-04-26 00:00:00
Tasks: Data Augmentation, Deep Learning, Diversity, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also requires data augmentation to extend diversity for preventing overfitting. However, the existing methods for data augmentation of medical image segmentation are mainly based on models which need to update parameters and cost extra computing resources. We proposed data augmentation methods designed to train a high accuracy deep learning network for medical image segmentation. The proposed data augmentation approaches are called KeepMask and KeepMix, which can create medical images by better identifying the boundary of the organ with no more parameters. Our methods achieved better performance and obtained more precise boundaries for medical image segmentation on datasets. The dice coefficient of our methods achieved 94.15% (3.04% higher than baseline) on CHAOS and 74.70% (5.25% higher than baseline) on MSD spleen with Unet.
Effect of latent space distribution on the segmentation of images with multiple annotations
Authors: Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever, Hugo J. Kuijf
Date: 2023-04-26 00:00:00
Tasks: Diversity

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Authors: Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Qiufang Ma, Xuhan Sheng, Ming Cheng, Haoyu Ma, Shijie Zhao, Jian Zhang, Junlin Li, Li Zhang
Date: 2023-04-26 00:00:00
Tasks: Image Super-Resolution, Position, Super-Resolution

360{\deg} omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower angular resolution due to being captured by fisheye lenses with the same sensor size for capturing planar images. To solve the above issues, we propose a two-stage framework for 360{\deg} omnidirectional image superresolution. The first stage employs two branches: model A, which incorporates omnidirectional position-aware deformable blocks (OPDB) and Fourier upsampling, and model B, which adds a spatial frequency fusion module (SFF) to model A. Model A aims to enhance the feature extraction ability of 360{\deg} image positional information, while Model B further focuses on the high-frequency information of 360{\deg} images. The second stage performs same-resolution enhancement based on the structure of model A with a pixel unshuffle operation. In addition, we collected data from YouTube to improve the fitting ability of the transformer, and created pseudo low-resolution images using a degradation network. Our proposed method achieves superior performance and wins the NTIRE 2023 challenge of 360{\deg} omnidirectional image super-resolution.
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
Authors: Nikola Zubić, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza
Date: 2023-04-26 00:00:00
Proceeding: ICCV 2023 1
Tasks: Event-based vision, object-detection, Object Detection, Object Recognition, Representation Learning

Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. This work eliminates this bottleneck by selecting representations based on the Gromov-Wasserstein Discrepancy (GWD) between raw events and their representation. It is about 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, datasets, and tasks. Thus finding representations with high task scores is equivalent to finding representations with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. Our optimized representations outperform existing representations by 1.7 mAP on the 1 Mpx dataset and 0.3 mAP on the Gen1 dataset, two established object detection benchmarks, and reach a 3.8% higher classification score on the mini N-ImageNet benchmark. Moreover, we outperform state-of-the-art by 2.1 mAP on Gen1 and state-of-the-art feed-forward methods by 6.0 mAP on the 1 Mpx datasets. This work opens a new unexplored field of explicit representation optimization for event-based learning.
Neural-PBIR Reconstruction of Shape, Material, and Illumination
Authors: Cheng Sun, Guangyan Cai, Zhengqin Li, Kai Yan, Cheng Zhang, Carl Marshall, Jia-Bin Huang, Shuang Zhao, Zhao Dong
Date: 2023-04-26 00:00:00
Proceeding: ICCV 2023 1
Tasks: Depth Prediction, Image Relighting, Inverse Rendering, Object, Object Reconstruction, Surface Normals Estimation, Surface Reconstruction

Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce an accurate and highly efficient object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Our pipeline firstly leverages a neural SDF based shape reconstruction to produce high-quality but potentially imperfect object shape. Then, we introduce a neural material and lighting distillation stage to achieve high-quality predictions for material and illumination. In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination. Experimental results demonstrate our pipeline significantly outperforms existing methods quality-wise and performance-wise.
Compensation Learning in Semantic Segmentation
Authors: Timo Kaiser, Christoph Reinders, Bodo Rosenhahn
Date: 2023-04-26 00:00:00
Tasks: Segmentation, Semantic Segmentation

Label noise and ambiguities between similar classes are challenging problems in developing new models and annotating new data for semantic segmentation. In this paper, we propose Compensation Learning in Semantic Segmentation, a framework to identify and compensate ambiguities as well as label noise. More specifically, we add a ground truth depending and globally learned bias to the classification logits and introduce a novel uncertainty branch for neural networks to induce the compensation bias only to relevant regions. Our method is employed into state-of-the-art segmentation frameworks and several experiments demonstrate that our proposed compensation learns inter-class relations that allow global identification of challenging ambiguities as well as the exact localization of subsequent label noise. Additionally, it enlarges robustness against label noise during training and allows target-oriented manipulation during inference. We evaluate the proposed method on %the widely used datasets Cityscapes, KITTI-STEP, ADE20k, and COCO-stuff10k.
Training-Free Location-Aware Text-to-Image Synthesis
Authors: Jiafeng Mao, Xueting Wang
Date: 2023-04-26 00:00:00
Tasks: Image Generation, object-detection, Object Detection, Position

Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts. However, they lack the ability to precisely control the size and position of objects in the generated image. In this study, we analyze the generative mechanism of the stable diffusion model and propose a new interactive generation paradigm that allows users to specify the position of generated objects without additional training. Moreover, we propose an object detection-based evaluation metric to assess the control capability of location aware generation task. Our experimental results show that our method outperforms state-of-the-art methods on both control capacity and image quality.
Learnable Ophthalmology SAM
Authors: Zhongxi Qiu, Yan Hu, Heng Li, Jiang Liu
Date: 2023-04-26 00:00:00
Tasks: Segmentation

Segmentation is vital for ophthalmology image analysis. But its various modal images hinder most of the existing segmentation algorithms applications, as they rely on training based on a large number of labels or hold weak generalization ability. Based on Segment Anything (SAM), we propose a simple but effective learnable prompt layer suitable for multiple target segmentation in ophthalmology multi-modal images, named Learnable Ophthalmology Segment Anything (SAM). The learnable prompt layer learns medical prior knowledge from each transformer layer. During training, we only train the prompt layer and task head based on a one-shot mechanism. We demonstrate the effectiveness of our thought based on four medical segmentation tasks based on nine publicly available datasets. Moreover, we only provide a new improvement thought for applying the existing fundamental CV models in the medical field. Our codes are available at \href{https://github.com/Qsingle/LearnablePromptSAM}{website}.
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Authors: Marco Huber, Meiling Fang, Fadi Boutros, Naser Damer
Date: 2023-04-26 00:00:00
Tasks: Face Presentation Attack Detection, Face Recognition

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations.
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Authors: Shitong Shao, Xiaohan Yuan, Zhen Huang, Ziming Qiu, Shuai Wang, Kevin Zhou
Date: 2023-04-26 00:00:00
Tasks: Diversity, Image Generation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powerful image synthesis performance, even better than Generative Adversarial Networks. Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks. After that, DiffuseExpand chooses high-quality samples to further enhance the effectiveness of data expansion. Our comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness of DiffuseExpand. Our code is released at https://github.com/shaoshitong/DiffuseExpand.
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
Authors: Marco Huber, Anh Thi Luu, Philipp Terhörst, Naser Damer
Date: 2023-04-26 00:00:00
Tasks: Face Recognition, Face Verification

Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important to operators, users, anddevelopers to increase trust, accountability, develop better systems, and highlight unfair behavior. In this work, we propose xSSAB, an approach to back-propagate similarity score-based arguments that support or oppose the face matching decision to visualize spatial maps that indicate similar and dissimilar areas as interpreted by the underlying FR model. Furthermore, we present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol, the first quantitative evaluation of the validity of similarity and dissimilarity maps in explainable face recognition approaches. We compare our efficient approach to state-of-the-art approaches demonstrating a superior trade-off between efficiency and performance. The code as well as the proposed Patch-LFW is publicly available at: https://github.com/marcohuber/xSSAB.
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Authors: Paweł Foszner, Agnieszka Szczęsna, Luca Ciampi, Nicola Messina, Adam Cygan, Bartosz Bizoń, Michał Cogiel, Dominik Golba, Elżbieta Macioszek, Michał Staniszewski
Date: 2023-04-26 00:00:00
Tasks: Multiple People Tracking, Unity

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.
STIR: Siamese Transformer for Image Retrieval Postprocessing
Authors: Aleksei Shabanov, Aleksei Tarasov, Sergey Nikolenko
Date: 2023-04-26 00:00:00
Tasks: Image Retrieval, Metric Learning, Representation Learning, Reranking, Re-Ranking, Retrieval, Triplet

Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the art methods such as HypViT move to more complex embedding spaces that may yield better results but are harder to scale to production environments. In this work, we first construct a simpler model based on triplet loss with hard negatives mining that performs at the state of the art level but does not have these drawbacks. Second, we introduce a novel approach for image retrieval postprocessing called Siamese Transformer for Image Retrieval (STIR) that reranks several top outputs in a single forward pass. Unlike previously proposed Reranking Transformers, STIR does not rely on global/local feature extraction and directly compares a query image and a retrieved candidate on pixel level with the usage of attention mechanism. The resulting approach defines a new state of the art on standard image retrieval datasets: Stanford Online Products and DeepFashion In-shop. We also release the source code at https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/ and an interactive demo of our approach at https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/
Group Equivariant BEV for 3D Object Detection
Authors: Hongwei Liu, Jian Yang, Jianfeng Zhang, Dongheng Shao, Jielong Guo, Shaobo Li, Xuan Tang, Xian Wei
Date: 2023-04-26 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Sensor Fusion

Recently, 3D object detection has attracted significant attention and achieved continuous improvement in real road scenarios. The environmental information is collected from a single sensor or multi-sensor fusion to detect interested objects. However, most of the current 3D object detection approaches focus on developing advanced network architectures to improve the detection precision of the object rather than considering the dynamic driving scenes, where data collected from sensors equipped in the vehicle contain various perturbation features. As a result, existing work cannot still tackle the perturbation issue. In order to solve this problem, we propose a group equivariant bird's eye view network (GeqBevNet) based on the group equivariant theory, which introduces the concept of group equivariant into the BEV fusion object detection network. The group equivariant network is embedded into the fused BEV feature map to facilitate the BEV-level rotational equivariant feature extraction, thus leading to lower average orientation error. In order to demonstrate the effectiveness of the GeqBevNet, the network is verified on the nuScenes validation dataset in which mAOE can be decreased to 0.325. Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Authors: Hongxiang Lin, Matteo Figini, Felice D'Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
Date: 2023-04-26 00:00:00
Tasks: Diagnostic, Image Enhancement

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.