pwc-archive/papers-with-abstracts huggingface dataset:
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification Tasks
Authors: Mohsen Hami, Mahdi JameBozorg
Date: 2024-04-16 00:00:00
Tasks: Binary Classification, Classification, Defect Detection, Denoising, image-classification, Image Classification, Image Denoising, SSIM

Images captured from the real world are often affected by different types of noise, which can significantly impact the performance of Computer Vision systems and the quality of visual data. This study presents a novel approach for defect detection in casting product noisy images, specifically focusing on submersible pump impellers. The methodology involves utilizing deep learning models such as VGG16, InceptionV3, and other models in both the spatial and frequency domains to identify noise types and defect status. The research process begins with preprocessing images, followed by applying denoising techniques tailored to specific noise categories. The goal is to enhance the accuracy and robustness of defect detection by integrating noise detection and denoising into the classification pipeline. The study achieved remarkable results using VGG16 for noise type classification in the frequency domain, achieving an accuracy of over 99%. Removal of salt and pepper noise resulted in an average SSIM of 87.9, while Gaussian noise removal had an average SSIM of 64.0, and periodic noise removal yielded an average SSIM of 81.6. This comprehensive approach showcases the effectiveness of the deep AutoEncoder model and median filter, for denoising strategies in real-world industrial applications. Finally, our study reports significant improvements in binary classification accuracy for defect detection compared to previous methods. For the VGG16 classifier, accuracy increased from 94.6% to 97.0%, demonstrating the effectiveness of the proposed noise detection and denoising approach. Similarly, for the InceptionV3 classifier, accuracy improved from 84.7% to 90.0%, further validating the benefits of integrating noise analysis into the classification pipeline.
Contextrast: Contextual Contrastive Learning for Semantic Segmentation
Authors: Changki Sung, Wanhee Kim, Jungho An, Wooju Lee, Hyungtae Lim, Hyun Myung
Date: 2024-04-16 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Segmentation, Semantic Segmentation

Despite great improvements in semantic segmentation, challenges persist because of the lack of local/global contexts and the relationship between them. In this paper, we propose Contextrast, a contrastive learning-based semantic segmentation method that allows to capture local/global contexts and comprehend their relationships. Our proposed method comprises two parts: a) contextual contrastive learning (CCL) and b) boundary-aware negative (BANE) sampling. Contextual contrastive learning obtains local/global context from multi-scale feature aggregation and inter/intra-relationship of features for better discrimination capabilities. Meanwhile, BANE sampling selects embedding features along the boundaries of incorrectly predicted regions to employ them as harder negative samples on our contrastive learning, resolving segmentation issues along the boundary region by exploiting fine-grained details. We demonstrate that our Contextrast substantially enhances the performance of semantic segmentation networks, outperforming state-of-the-art contrastive learning approaches on diverse public datasets, e.g. Cityscapes, CamVid, PASCAL-C, COCO-Stuff, and ADE20K, without an increase in computational cost during inference.
Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
Authors: John Francis, Stephen Law
Date: 2024-04-16 00:00:00
Tasks: Domain Adaptation, Image-to-Image Translation, Unsupervised Domain Adaptation

We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks
Authors: Florian Barthel, Arian Beckmann, Wieland Morgenstern, Anna Hilsmann, Peter Eisert
Date: 2024-04-16 00:00:00
Tasks: 3DGS, Decoder, Diversity, NeRF

NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providing an explicit 3D representation that can be rendered efficiently at high frame rates. In this work, we present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally, our approach allows for a high resolution GAN inversion and real-time GAN editing with 3D Gaussian Splatting scenes. Project page: florian-barthel.github.io/gaussian_decoder
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
Authors: Sinisa Stekovic, Stefan Ainetter, Mattia D'Urso, Friedrich Fraundorfer, Vincent Lepetit
Date: 2024-04-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Retrieval, Scene Understanding

We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects from images using interpretable shape programs. In comparison to traditional CAD model retrieval methods, the use of shape programs for 3D reconstruction allows for reasoning about the semantic properties of reconstructed objects, editing, low memory footprint, etc. However, the utilization of shape programs for 3D scene understanding has been largely neglected in past works. As our main contribution, we enable gradient-based optimization by introducing a module that translates shape programs designed in Blender, for example, into efficient PyTorch code. We also provide a method that relies on PyTorchGeoNodes and is inspired by Monte Carlo Tree Search (MCTS) to jointly optimize discrete and continuous parameters of shape programs and reconstruct 3D objects for input scenes. In our experiments, we apply our algorithm to reconstruct 3D objects in the ScanNet dataset and evaluate our results against CAD model retrieval-based reconstructions. Our experiments indicate that our reconstructions match well the input scenes while enabling semantic reasoning about reconstructed objects.
CorrespondentDream: Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Authors: SeungWook Kim, Kejie Li, Xueqing Deng, Yichun Shi, Minsu Cho, Peng Wang
Date: 2024-04-16 00:00:00
Proceeding: CVPR 2024 1
Tasks: Common Sense Reasoning, NeRF, Text to 3D

Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e.g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models. However, the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic, the underlying geometry may contain errors such as unreasonable concavities. In this work, we propose CorrespondentDream, an effective method to leverage annotation-free, cross-view correspondences yielded from the diffusion U-Net to provide additional 3D prior to the NeRF optimization process. We find that these correspondences are strongly consistent with human perception, and by adopting it in our loss design, we are able to produce NeRF models with geometries that are more coherent with common sense, e.g., more smoothed object surface, yielding higher 3D fidelity. We demonstrate the efficacy of our approach through various comparative qualitative results and a solid user study.
Intra-operative tumour margin evaluation in breast-conserving surgery with deep learning
Authors: Wei-Chung Shia, Yu-Len Huang, Yi-Chun Chen, Hwa-Koon Wu, Dar-Ren Chen
Date: 2024-04-16 00:00:00

A positive margin may result in an increased risk of local recurrences after breast retention surgery for any malignant tumour. In order to reduce the number of positive margins would offer surgeon real-time intra-operative information on the presence of positive resection margins. This study aims to design an intra-operative tumour margin evaluation scheme by using specimen mammography in breast-conserving surgery. Total of 30 cases were evaluated and compared with the manually determined contours by experienced physicians and pathology report. The proposed method utilizes image thresholding to extract regions of interest and then performs a deep learning model, i.e. SegNet, to segment tumour tissue. The margin width of normal tissues surrounding it is evaluated as the result. The desired size of margin around the tumor was set for 10 mm. The smallest average difference to manual sketched margin (6.53 mm +- 5.84). In the all case, the SegNet architecture was utilized to obtain tissue specimen boundary and tumor contour, respectively. The simulation results indicated that this technology is helpful in discriminating positive from negative margins in the intra-operative setting. The aim of proposed scheme was a potential procedure in the intra-operative measurement system. The experimental results reveal that deep learning techniques can draw results that are consistent with pathology reports.
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Authors: Kai Chen, Yanze Li, Wenhua Zhang, Yanxin Liu, Pengxiang Li, Ruiyuan Gao, Lanqing Hong, Meng Tian, Xinhai Zhao, Zhenguo Li, Dit-yan Yeung, Huchuan Lu, Xu Jia
Date: 2024-04-16 00:00:00
Tasks: Autonomous Driving, Visual Reasoning

Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted capabilities in natural circumstances, lacking automated and quantifiable assessment for self-driving, let alone the severe road corner cases. In this work, we propose CODA-LM, the very first benchmark for the automatic evaluation of LVLMs for self-driving corner cases. We adopt a hierarchical data structure and prompt powerful LVLMs to analyze complex driving scenes and generate high-quality pre-annotations for the human annotators, while for LVLM evaluation, we show that using the text-only large language models (LLMs) as judges reveals even better alignment with human preferences than the LVLM judges. Moreover, with our CODA-LM, we build CODA-VLM, a new driving LVLM surpassing all open-sourced counterparts on CODA-LM. Our CODA-VLM performs comparably with GPT-4V, even surpassing GPT-4V by +21.42% on the regional perception task. We hope CODA-LM can become the catalyst to promote interpretable self-driving empowered by LVLMs.
Do Counterfactual Examples Complicate Adversarial Training?
Authors: Eric Yeats, Cameron Darwin, Eduardo Ortega, Frank Liu, Hai Li
Date: 2024-04-16 00:00:00
Tasks: counterfactual

We leverage diffusion models to study the robustness-performance tradeoff of robust classifiers. Our approach introduces a simple, pretrained diffusion method to generate low-norm counterfactual examples (CEs): semantically altered data which results in different true class membership. We report that the confidence and accuracy of robust models on their clean training data are associated with the proximity of the data to their CEs. Moreover, robust models perform very poorly when evaluated on the CEs directly, as they become increasingly invariant to the low-norm, semantic changes brought by CEs. The results indicate a significant overlap between non-robust and semantic features, countering the common assumption that non-robust features are not interpretable.
ReWiTe: Realistic Wide-angle and Telephoto Dual Camera Fusion Dataset via Beam Splitter Camera Rig
Authors: Chunli Peng, Xuan Dong, Tiantian Cao, Zhengqing Li, Kun Dong, Weixin Li
Date: 2024-04-16 00:00:00

The fusion of images from dual camera systems featuring a wide-angle and a telephoto camera has become a hotspot problem recently. By integrating simultaneously captured wide-angle and telephoto images from these systems, the resulting fused image achieves a wide field of view (FOV) coupled with high-definition quality. Existing approaches are mostly deep learning methods, and predominantly rely on supervised learning, where the training dataset plays a pivotal role. However, current datasets typically adopt a data synthesis approach generate input pairs of wide-angle and telephoto images alongside ground-truth images. Notably, the wide-angle inputs are synthesized rather than captured using real wide-angle cameras, and the ground-truth image is captured by wide-angle camera whose quality is substantially lower than that of input telephoto images captured by telephoto cameras. To address these limitations, we introduce a novel hardware setup utilizing a beam splitter to simultaneously capture three images, i.e. input pairs and ground-truth images, from two authentic cellphones equipped with wide-angle and telephoto dual cameras. Specifically, the wide-angle and telephoto images captured by cellphone 2 serve as the input pair, while the telephoto image captured by cellphone 1, which is calibrated to match the optical path of the wide-angle image from cellphone 2, serves as the ground-truth image, maintaining quality on par with the input telephoto image. Experiments validate the efficacy of our newly introduced dataset, named ReWiTe, significantly enhances the performance of various existing methods for real-world wide-angle and telephoto dual image fusion tasks.
Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation
Authors: Aaron Kujawa, Reuben Dorent, Sebastien Ourselin, Tom Vercauteren
Date: 2024-04-16 00:00:00
Tasks: GPU, Segmentation, Semantic Segmentation

Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduces the effective number of labels required for learning-based whole brain parcellation and then recovers original labels. Using a greedy graph colouring algorithm, our method automatically groups and merges multiple spatially separate labels prior to model training and inference. The merged labels may be semantically unrelated. A deep learning model is trained to predict merged labels. At inference time, original labels are restored using atlas-based influence regions. In our experiments, the proposed approach reduces the number of labels by up to 68% while achieving segmentation accuracy comparable to the baseline method without label merging and splitting. Moreover, model training and inference times as well as GPU memory requirements were reduced significantly. The proposed method can be applied to all semantic segmentation tasks with a large number of spatially separate classes within an atlas-based prior.
CMU-Flownet: Exploring Point Cloud Scene Flow Estimation in Occluded Scenario
Authors: Jingze Chen, Junfeng Yao, Qiqin Lin, Lei LI
Date: 2024-04-16 00:00:00
Tasks: Occlusion Estimation, Occlusion Handling, Scene Flow Estimation

Occlusions hinder point cloud frame alignment in LiDAR data, a challenge inadequately addressed by scene flow models tested mainly on occlusion-free datasets. Attempts to integrate occlusion handling within networks often suffer accuracy issues due to two main limitations: a) the inadequate use of occlusion information, often merging it with flow estimation without an effective integration strategy, and b) reliance on distance-weighted upsampling that falls short in correcting occlusion-related errors. To address these challenges, we introduce the Correlation Matrix Upsampling Flownet (CMU-Flownet), incorporating an occlusion estimation module within its cost volume layer, alongside an Occlusion-aware Cost Volume (OCV) mechanism. Specifically, we propose an enhanced upsampling approach that expands the sensory field of the sampling process which integrates a Correlation Matrix designed to evaluate point-level similarity. Meanwhile, our model robustly integrates occlusion data within the context of scene flow, deploying this information strategically during the refinement phase of the flow estimation. The efficacy of this approach is demonstrated through subsequent experimental validation. Empirical assessments reveal that CMU-Flownet establishes state-of-the-art performance within the realms of occluded Flyingthings3D and KITTY datasets, surpassing previous methodologies across a majority of evaluated metrics.
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks
Authors: Malte Rippa, Ruben Schulze, Marian Himstedt, Felice Burn
Date: 2024-04-16 00:00:00
Tasks: Data Augmentation

Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved by a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214.
SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments
Authors: Niklas Gard, Anna Hilsmann, Peter Eisert
Date: 2024-04-16 00:00:00
Tasks: 6D Pose Estimation, Camera Localization, Indoor Localization, Indoor Localization (3-DoF Pose: X, Y, Yaw), Indoor Localization (6-DoF Pose), Visual Localization

In this paper, we present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training. Our approach employs a novel matching procedure to localize the perspective camera's viewport, given as an RGB image, within a set of panoramic semantic layout representations of the indoor environment. The panoramas are rendered from an untextured 3D reference model, which only comprises approximate structural information about room shapes, along with door and window annotations. We demonstrate that a straightforward convolutional network structure can successfully achieve image-to-panorama and ultimately image-to-model matching. Through a viewport classification score, we rank reference panoramas and select the best match for the query image. Then, a 6D relative pose is estimated between the chosen panorama and query image. Our experiments demonstrate that this approach not only efficiently bridges the domain gap but also generalizes well to previously unseen scenes that are not part of the training data. Moreover, it achieves superior localization accuracy compared to the state of the art methods and also estimates more degrees of freedom of the camera pose. Our source code is publicly available at https://fraunhoferhhi.github.io/spvloc .
MobileNetV4 -- Universal Models for the Mobile Ecosystem
Authors: Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, Vaibhav Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard
Date: 2024-04-16 00:00:00
Tasks: Image Classification, Neural Architecture Search

We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Depthwise (ExtraDW) variant. Alongside UIB, we present Mobile MQA, an attention block tailored for mobile accelerators, delivering a significant 39% speedup. An optimized neural architecture search (NAS) recipe is also introduced which improves MNv4 search effectiveness. The integration of UIB, Mobile MQA and the refined NAS recipe results in a new suite of MNv4 models that are mostly Pareto optimal across mobile CPUs, DSPs, GPUs, as well as specialized accelerators like Apple Neural Engine and Google Pixel EdgeTPU - a characteristic not found in any other models tested. Finally, to further boost accuracy, we introduce a novel distillation technique. Enhanced by this technique, our MNv4-Hybrid-Large model delivers 87% ImageNet-1K accuracy, with a Pixel 8 EdgeTPU runtime of just 3.8ms.
Enhancing Sign Language Teaching: A Mixed Reality Approach for Immersive Learning and Multi-Dimensional Feedback
Authors: Hongli Wen, Yang Xu, Lin Li, Xudong Ru, Xingce Wang, Zhongke Wu
Date: 2024-04-16 00:00:00
Tasks: Mixed Reality

Traditional sign language teaching methods face challenges such as limited feedback and diverse learning scenarios. Although 2D resources lack real-time feedback, classroom teaching is constrained by a scarcity of teacher. Methods based on VR and AR have relatively primitive interaction feedback mechanisms. This study proposes an innovative teaching model that uses real-time monocular vision and mixed reality technology. First, we introduce an improved hand-posture reconstruction method to achieve sign language semantic retention and real-time feedback. Second, a ternary system evaluation algorithm is proposed for a comprehensive assessment, maintaining good consistency with experts in sign language. Furthermore, we use mixed reality technology to construct a scenario-based 3D sign language classroom and explore the user experience of scenario teaching. Overall, this paper presents a novel teaching method that provides an immersive learning experience, advanced posture reconstruction, and precise feedback, achieving positive feedback on user experience and learning effectiveness.