pwc-archive/papers-with-abstracts huggingface dataset:
2D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scans
Authors: Fares Bougourzi, Fadi Dornaika, Amir Nakib, Cosimo Distante, Abdelmalik Taleb-Ahmed
Date: 2023-03-15 00:00:00
Tasks: Prediction, severity prediction

Since the appearance of Covid-19 in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 3nd COV19D competition for Covid-19 Severity Prediction. In order to deal with the big gap between the validation and test results that were shown in the previous version of this competition, we proposed to combine the prediction of 2D and 3D CNN predictions. For the 2D CNN approach, we propose 2B-InceptResnet architecture which consists of two paths for segmented lungs and infection of all slices of the input CT-scan, respectively. Each path consists of ConvLayer and Inception-ResNet pretrained model on ImageNet. For the 3D CNN approach, we propose hybrid-DeCoVNet architecture which consists of four blocks: Stem, four 3D-ResNet layers, Classification Head and Decision layer. Our proposed approaches outperformed the baseline approach in the validation data of the 3nd COV19D competition for Covid-19 Severity Prediction by 36%.
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Authors: HUI ZHANG, Zheng Wang, Dan Zeng, Zuxuan Wu, Yu-Gang Jiang
Date: 2023-03-15 00:00:00
Tasks: Anomaly Detection, Denoising, Unsupervised Anomaly Detection

Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD
Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features
Authors: Choubo Ding, Guansong Pang
Date: 2023-03-15 00:00:00
Tasks: Disentanglement, Object Recognition, Out-of-Distribution Detection

Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground features (e.g., objects in CIFAR100 images vs. those in CIFAR10 images) and background features (e.g., textural images vs. objects in CIFAR10). Existing methods can confound foreground and background features in training, failing to utilize the background features for OOD detection. This paper considers the importance of feature disentanglement in out-of-distribution detection and proposes the simultaneous exploitation of both foreground and background features to support the detection of OOD inputs in in out-of-distribution detection. To this end, we propose a novel framework that first disentangles foreground and background features from ID training samples via a dense prediction approach, and then learns a new classifier that can evaluate the OOD scores of test images from both foreground and background features. It is a generic framework that allows for a seamless combination with various existing OOD detection methods. Extensive experiments show that our approach 1) can substantially enhance the performance of four different state-of-the-art (SotA) OOD detection methods on multiple widely-used OOD datasets with diverse background features, and 2) achieves new SotA performance on these benchmarks.
Re-ReND: Real-time Rendering of NeRFs across Devices
Authors: Sara Rojas, Jesus Zarzar, Juan Camilo Perez, Artsiom Sanakoyeu, Ali Thabet, Albert Pumarola, Bernard Ghanem
Date: 2023-03-15 00:00:00
Proceeding: ICCV 2023 1
Tasks: NeRF

This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling Real-time Rendering of NeRFs across Devices. Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines. The proposed method distills the NeRF by extracting the learned density into a mesh, while the learned color information is factorized into a set of matrices that represent the scene's light field. Factorization implies the field is queried via inexpensive MLP-free matrix multiplications, while using a light field allows rendering a pixel by querying the field a single time-as opposed to hundreds of queries when employing a radiance field. Since the proposed representation can be implemented using a fragment shader, it can be directly integrated with standard rasterization frameworks. Our flexible implementation can render a NeRF in real-time with low memory requirements and on a wide range of resource-constrained devices, including mobiles and AR/VR headsets. Notably, we find that Re-ReND can achieve over a 2.6-fold increase in rendering speed versus the state-of-the-art without perceptible losses in quality.
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
Authors: Shuyao Shang, Zhengyang Shan, Guangxing Liu, LunQian Wang, XingHua Wang, Zekai Zhang, Jinglin Zhang
Date: 2023-03-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content. Therefore, we present ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for Single Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN, which restores primary low-frequency components, and a DPM, which predicts the residual between the ground-truth image and the CNN predicted image. In contrast to the common diffusion-based methods that directly use LR images to guide the noise towards HR space, ResDiff utilizes the CNN's initial prediction to direct the noise towards the residual space between HR space and CNN-predicted space, which not only accelerates the generation process but also acquires superior sample quality. Additionally, a frequency-domain-based loss function for CNN is introduced to facilitate its restoration, and a frequency-domain guided diffusion is designed for DPM on behalf of predicting high-frequency details. The extensive experiments on multiple benchmark datasets demonstrate that ResDiff outperforms previous diffusion based methods in terms of shorter model convergence time, superior generation quality, and more diverse samples.
Multi-Exposure HDR Composition by Gated Swin Transformer
Authors: Rui Zhou, Yan Niu
Date: 2023-03-15 00:00:00
Tasks: feature selection, Image Generation

Fusing a sequence of perfectly aligned images captured at various exposures, has shown great potential to approach High Dynamic Range (HDR) imaging by sensors with limited dynamic range. However, in the presence of large motion of scene objects or the camera, mis-alignment is almost inevitable and leads to the notorious ``ghost'' artifacts. Besides, factors such as the noise in the dark region or color saturation in the over-bright region may also fail to fill local image details to the HDR image. This paper provides a novel multi-exposure fusion model based on Swin Transformer. Particularly, we design feature selection gates, which are integrated with the feature extraction layers to detect outliers and block them from HDR image synthesis. To reconstruct the missing local details by well-aligned and properly-exposed regions, we exploit the long distance contextual dependency in the exposure-space pyramid by the self-attention mechanism. Extensive numerical and visual evaluation has been conducted on a variety of benchmark datasets. The experiments show that our model achieves the accuracy on par with current top performing multi-exposure HDR imaging models, while gaining higher efficiency.
RefiNeRF: Modelling dynamic neural radiance fields with inconsistent or missing camera parameters
Authors: Shuja Khalid, Frank Rudzicz
Date: 2023-03-15 00:00:00
Tasks: NeRF, Novel View Synthesis

Novel view synthesis (NVS) is a challenging task in computer vision that involves synthesizing new views of a scene from a limited set of input images. Neural Radiance Fields (NeRF) have emerged as a powerful approach to address this problem, but they require accurate knowledge of camera \textit{intrinsic} and \textit{extrinsic} parameters. Traditionally, structure-from-motion (SfM) and multi-view stereo (MVS) approaches have been used to extract camera parameters, but these methods can be unreliable and may fail in certain cases. In this paper, we propose a novel technique that leverages unposed images from dynamic datasets, such as the NVIDIA dynamic scenes dataset, to learn camera parameters directly from data. Our approach is highly extensible and can be integrated into existing NeRF architectures with minimal modifications. We demonstrate the effectiveness of our method on a variety of static and dynamic scenes and show that it outperforms traditional SfM and MVS approaches. The code for our method is publicly available at \href{https://github.com/redacted/refinerf}{https://github.com/redacted/refinerf}. Our approach offers a promising new direction for improving the accuracy and robustness of NVS using NeRF, and we anticipate that it will be a valuable tool for a wide range of applications in computer vision and graphics.
SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
Authors: Siqi Fan, Zhe Wang, Yan Wang, Jingjing Liu
Date: 2023-03-15 00:00:00
Tasks: Data Augmentation, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Thermal Image Segmentation

For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology in challenging lighting conditions. Thermal signal is an informative additional channel that can bring to light the contour and fine-grained texture of blurred regions in low-quality RGB image. Aiming at practical RGB-T (thermal) segmentation, we systematically propose a Spatial-aware Demand-guided Recursive Meshing (SpiderMesh) framework that: 1) proactively compensates inadequate contextual semantics in optically-impaired regions via a demand-guided target masking algorithm; 2) refines multimodal semantic features with recursive meshing to improve pixel-level semantic analysis performance. We further introduce an asymmetric data augmentation technique M-CutOut, and enable semi-supervised learning to fully utilize RGB-T labels only sparsely available in practical use. Extensive experiments on MFNet and PST900 datasets demonstrate that SpiderMesh achieves state-of-the-art performance on standard RGB-T segmentation benchmarks.
Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency
Authors: Runzhou Tao, Wencheng Han, Zhongying Qiu, Cheng-Zhong Xu, Jianbing Shen
Date: 2023-03-15 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Object Detection, Monocular 3D Object Detection, object-detection, Object Detection

Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels. https://github.com/weakmono3d/weakmono3d
Making Vision Transformers Efficient from A Token Sparsification View
Authors: Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou
Date: 2023-03-15 00:00:00
Proceeding: CVPR 2023 1
Tasks: Efficient ViTs, image-classification, Image Classification, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation, Video Recognition

The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vision transformer, and (iii) non-general-purpose networks for downstream tasks. In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks. The semantic tokens represent cluster centers, and they are initialized by pooling image tokens in space and recovered by attention, which can adaptively represent global or local semantic information. Due to the cluster properties, a few semantic tokens can attain the same effect as vast image tokens, for both global and local vision transformers. For instance, only 16 semantic tokens on DeiT-(Tiny,Small,Base) can achieve the same accuracy with more than 100% inference speed improvement and nearly 60% FLOPs reduction; on Swin-(Tiny,Small,Base), we can employ 16 semantic tokens in each window to further speed it up by around 20% with slight accuracy increase. Besides great success in image classification, we also extend our method to video recognition. In addition, we design a STViT-R(ecover) network to restore the detailed spatial information based on the STViT, making it work for downstream tasks, which is powerless for previous token sparsification methods. Experiments demonstrate that our method can achieve competitive results compared to the original networks in object detection and instance segmentation, with over 30% FLOPs reduction for backbone. Code is available at http://github.com/changsn/STViT-R
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters
Authors: Wenqi Ouyang, Yi Dong, Xiaoyang Kang, Peiran Ren, Xin Xu, Xuansong Xie
Date: 2023-03-15 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Enhancement, Image Retouching, Photo Retouching

Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to conveniently edit their images simultaneously. Recent white-box retouching methods rely on cascaded global filters that provide image-level filter arguments but cannot perform fine-grained retouching. In contrast, colorists typically employ a divide-and-conquer approach, performing a series of region-specific fine-grained enhancements when using traditional tools like Davinci Resolve. We draw on this insight to develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet. Our model generates filter arguments (e.g., saturation, contrast, hue) and attention maps of regions for each filter simultaneously. Instead of cascading filters, RSFNet employs linear summations of filters, allowing for a more diverse range of filter classes that can be trained more easily. Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and increased user convenience for editable white-box retouching.
Visual Prompt Based Personalized Federated Learning
Authors: Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, DaCheng Tao
Date: 2023-03-15 00:00:00
Tasks: Federated Learning, image-classification, Image Classification, Personalized Federated Learning

As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowledge from all distributed clients. Most existing PFL algorithms tackle personalization in a model-centric way, such as personalized layer partition, model regularization, and model interpolation, which all fail to take into account the data characteristics of distributed clients. In this paper, we propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients and provides that information to the aggregation model to help with classification tasks. Specifically, in each round of pFedPT training, each client generates a local personalized prompt related to local data distribution. Then, the local model is trained on the input composed of raw data and a visual prompt to learn the distribution information contained in the prompt. During model testing, the aggregated model obtains prior knowledge of the data distributions based on the prompts, which can be seen as an adaptive fine-tuning of the aggregation model to improve model performances on different clients. Furthermore, the visual prompt can be added as an orthogonal method to implement personalization on the client for existing FL methods to boost their performance. Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Authors: Yuhang Lu, Touradj Ebrahimi
Date: 2023-03-15 00:00:00
Tasks: Face Recognition, Knowledge Distillation, Super-Resolution

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry
Authors: Jiaxu Zhang, Junwu Weng, Di Kang, Fang Zhao, Shaoli Huang, Xuefei Zhe, Linchao Bao, Ying Shan, Jue Wang, Zhigang Tu
Date: 2023-03-15 00:00:00
Proceeding: CVPR 2023 1
Tasks: motion retargeting

A good motion retargeting cannot be reached without reasonable consideration of source-target differences on both the skeleton and shape geometry levels. In this work, we propose a novel Residual RETargeting network (R2ET) structure, which relies on two neural modification modules, to adjust the source motions to fit the target skeletons and shapes progressively. In particular, a skeleton-aware module is introduced to preserve the source motion semantics. A shape-aware module is designed to perceive the geometries of target characters to reduce interpenetration and contact-missing. Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing. To balance these two modifications, we further present a balancing gate to conduct linear interpolation between them. Extensive experiments on the public dataset Mixamo demonstrate that our R2ET achieves the state-of-the-art performance, and provides a good balance between the preservation of motion semantics as well as the attenuation of interpenetration and contact-missing. Code is available at https://github.com/Kebii/R2ET.
Economical Quaternion Extraction from a Human Skeletal Pose Estimate using 2-D Cameras
Authors: Sriram Radhakrishna, Adithya Balasubramanyam
Date: 2023-03-15 00:00:00
Tasks: Pose Estimation

In this paper, we present a novel algorithm to extract a quaternion from a two dimensional camera frame for estimating a contained human skeletal pose. The problem of pose estimation is usually tackled through the usage of stereo cameras and intertial measurement units for obtaining depth and euclidean distance for measurement of points in 3D space. However, the usage of these devices comes with a high signal processing latency as well as a significant monetary cost. By making use of MediaPipe, a framework for building perception pipelines for human pose estimation, the proposed algorithm extracts a quaternion from a 2-D frame capturing an image of a human object at a sub-fifty millisecond latency while also being capable of deployment at edges with a single camera frame and a generally low computational resource availability, especially for use cases involving last-minute detection and reaction by autonomous robots. The algorithm seeks to bypass the funding barrier and improve accessibility for robotics researchers involved in designing control systems.