pwc-archive/papers-with-abstracts huggingface dataset:
Stop overkilling simple tasks with black-box models and use transparent models instead
Authors: Matteo Rizzo, Matteo Marcuzzo, Alessandro Zangari, Andrea Gasparetto, Andrea Albarelli
Date: 2023-02-06 00:00:00
Tasks: Deep Learning, Feature Engineering

In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Authors: Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner
Date: 2023-02-06 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Survey

Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted to a closed set of semantic classes available in their training data, and are therefore unreliable when confronted with previously unseen instances. Thus, multiple perception datasets have been created for the evaluation of anomaly detection methods, which can be categorized into three groups: real anomalies in real-world, synthetic anomalies augmented into real-world and completely synthetic scenes. This survey provides a structured and, to the best of our knowledge, complete overview and comparison of perception datasets for anomaly detection in autonomous driving. Each chapter provides information about tasks and ground truth, context information, and licenses. Additionally, we discuss current weaknesses and gaps in existing datasets to underline the importance of developing further data.
AMD-HookNet for Glacier Front Segmentation
Authors: Fei Wu, Nora Gourmelon, Thorsten Seehaus, Jianlin Zhang, Matthias Braun, Andreas Maier, Vincent Christlein
Date: 2023-02-06 00:00:00
Tasks: Calving Front Delineation In Synthetic Aperture Radar Imagery, Calving Front Delineation In Synthetic Aperture Radar Imagery With Fixed Training Amount, Segmentation

Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions, however, it is not feasible to perform this task manually for all calving glaciers globally due to time-constraints. Deep learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery. The calving front is represented as a single thin line between the ocean and the glacier, which makes the task vulnerable to inaccurate predictions. The limited availability of annotated glacier imagery leads to a lack of data diversity (not all possible combinations of different weather conditions, terminus shapes, sensors, etc. are present in the data), which exacerbates the difficulty of accurate segmentation. In this paper, we propose Attention-Multi-hooking-Deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework for synthetic aperture radar (SAR) images. The proposed method aims to enhance the feature representation capability through multiple information interactions between low-resolution and high-resolution inputs based on a two-branch U-Net. The attention mechanism, integrated into the two branch U-Net, aims to interact between the corresponding coarse and fine-grained feature maps. This allows the network to automatically adjust feature relationships, resulting in accurate pixel-classification predictions. Extensive experiments and comparisons on the challenging glacier segmentation benchmark dataset CaFFe show that our AMD-HookNet achieves a mean distance error of 438 m to the ground truth outperforming the current state of the art by 42%, which validates its effectiveness.
PatchDCT: Patch Refinement for High Quality Instance Segmentation
Authors: Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang
Date: 2023-02-06 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation, Vocal Bursts Intensity Prediction

High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors. To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework. However, the vanilla combination does not bring significant gains, because changes in some elements of the DCT vector will affect the prediction of the entire mask. Thus, we propose a simple and novel method named PatchDCT, which separates the mask decoded from a DCT vector into several patches and refines each patch by the designed classifier and regressor. Specifically, the classifier is used to distinguish mixed patches from all patches, and to correct previously mispredicted foreground and background patches. In contrast, the regressor is used for DCT vector prediction of mixed patches, further refining the segmentation quality at boundary locations. Experiments on COCO show that our method achieves 2.0%, 3.2%, 4.5% AP and 3.4%, 5.3%, 7.0% Boundary AP improvements over Mask-RCNN on COCO, LVIS, and Cityscapes, respectively. It also surpasses DCT-Mask by 0.7%, 1.1%, 1.3% AP and 0.9%, 1.7%, 4.2% Boundary AP on COCO, LVIS and Cityscapes. Besides, the performance of PatchDCT is also competitive with other state-of-the-art methods.
HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination
Authors: Dr. Olivier Jaubert, Dr. Javier Montalt-Tordera, Dr. Daniel Knight, Pr. Simon Arridge, Dr. Jennifer Steeden, Pr. Vivek Muthurangu
Date: 2023-02-06 00:00:00
Tasks: SSIM

PURPOSE: Interactive cardiac magnetic resonance imaging is used for fast scan planning and MR guided interventions. However, the requirement for real-time acquisition and near real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). METHODS: A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework -HyperSLICE- was compared to conventional Cartesian real-time, and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post-hoc Nemenyi test (p<0.05). RESULTS: In simulations the NRMSE, pSNR, SSIM and LAPE were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (SSIM: 0.71 vs 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back showing sufficiently fast reconstruction for interactive imaging. CONCLUSION: HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared to radial and uniform spiral trajectories.
1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop
Authors: Qixun Wang, Xiaofeng Guo, Haofan Wang
Date: 2023-02-06 00:00:00
Tasks: Multi-class Classification, Panoptic Segmentation, Predicate Classification, Relation, Relation Network, Relation Prediction, Segmentation, Semantic Segmentation

Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-stage paradigm which simultaneously generates scene graphs and predicts semantic segmentation masks or two-stage paradigm that first adopt an off-the-shelf panoptic segmentor, then pairwise relationship prediction between these predicted objects. One-stage approach despite having a simplified training paradigm, its segmentation results are usually under-satisfactory, while two-stage approach lacks global context and leads to low performance on relation prediction. To bridge this gap, in this paper, we propose GRNet, a Global Relation Network in two-stage paradigm, where the pre-extracted local object features and their corresponding masks are fed into a transformer with class embeddings. To handle relation ambiguity and predicate classification bias caused by long-tailed distribution, we formulate relation prediction in the second stage as a multi-class classification task with soft label. We conduct comprehensive experiments on OpenPSG dataset and achieve the state-of-art performance on the leadboard. We also show the effectiveness of our soft label strategy for long-tailed classes in ablation studies. Our code has been released in https://github.com/wangqixun/mfpsg.
Approximation of radiative transfer for surface spectral features
Authors: Frédéric Schmidt
Date: 2023-02-06 00:00:00
Tasks: Form

Remote sensing hyperspectral and more generally spectral instruments are common tools to decipher surface features in Earth and Planetary science. While linear mixture is the most common approximation for compounds detection (mineral, water, ice, etc...), the transfer of light in surface and atmospheric medium are highly non-linear. The exact simulation of non-linearities can be estimated at very high numerical cost. Here I propose a very simple non-linear form (that includes the regular linear area mixture) of radiative transfer to approximate surface spectral feature. I demonstrate that this analytical form is able to approximate the grain size and intimate mixture dependence of surface features. In addition, the same analytical form can approximate the effect of Martian mineral aerosols. Unfortunately, Earth aerosols are more complex (water droplet, water ice, soot,...) and are not expected to follow the same trend.
COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images
Authors: Saddam Hussain Khan
Date: 2023-02-06 00:00:00
Tasks: Decoder, Diagnostic, Transfer Learning

COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread. In this regard, a new two-stage analysis framework is developed to analyze minute irregularities of COVID-19 infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is developed that incorporates the Split-Transform-Merge (STM) block and channel boosting (CB) to identify COVID-19 infected CT slices in the first stage. Each STM block extracts boundary and region-smoothing-specific features for COVID-19 infection detection. Moreover, the various boosted channels are obtained by introducing the new CB and Transfer Learning (TL) concept in STM blocks to capture small illumination and texture variations of COVID-19-specific images. The COVID-19 CTs are provided with new SA-CB-BRSeg segmentation CNN for delineating infection in images in the second stage. SA-CB-BRSeg methodically utilized smoothening and heterogeneous operations in the encoder and decoder to capture simultaneously COVID-19 specific patterns that are region homogeneity, texture variation, and boundaries. Additionally, the new CB concept is introduced in the decoder of SA-CB-BRSeg by combining additional channels using TL to learn the low contrast region. The proposed STM-BRNet and SA-CB-BRSeg yield considerable achievement in accuracy: 98.01 %, Recall: 98.12%, F-score: 98.11%, and Dice Similarity: 96.396%, IOU: 98.845 % for the COVID-19 infectious region, respectively. The proposed two-stage framework significantly increased performance compared to single-phase and other reported systems and reduced the burden on the radiologists.
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Authors: Jingyao Li, Pengguang Chen, Shaozuo Yu, Zexin He, Shu Liu, Jiaya Jia
Date: 2023-02-06 00:00:00
Proceeding: CVPR 2023 1
Tasks: All, Out-of-Distribution Detection

The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-based methods to learn the ID features, which tend to learn shortcuts instead of comprehensive representations. In this work, we find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We deeply explore the main contributors of OOD detection and find that reconstruction-based pretext tasks have the potential to provide a generally applicable and efficacious prior, which benefits the model in learning intrinsic data distributions of the ID dataset. Specifically, we take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD). Without bells and whistles, MOOD outperforms previous SOTA of one-class OOD detection by 5.7%, multi-class OOD detection by 3.0%, and near-distribution OOD detection by 2.1%. It even defeats the 10-shot-per-class outlier exposure OOD detection, although we do not include any OOD samples for our detection
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection
Authors: Menglong Chen, Xingtai Gui, Shicai Fan
Date: 2023-02-06 00:00:00
Tasks: Contrastive Learning, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Unsupervised out-of-distribution (OOD) Detection aims to separate the samples falling outside the distribution of training data without label information. Among numerous branches, contrastive learning has shown its excellent capability of learning discriminative representation in OOD detection. However, for its limited vision, merely focusing on instance-level relationship between augmented samples, it lacks attention to the relationship between samples with same semantics. Based on the classic contrastive learning, we propose Cluster-aware Contrastive Learning (CCL) framework for unsupervised OOD detection, which considers both instance-level and semantic-level information. Specifically, we study a cooperation strategy of clustering and contrastive learning to effectively extract the latent semantics and design a cluster-aware contrastive loss function to enhance OOD discriminative ability. The loss function can simultaneously pay attention to the global and local relationships by treating both the cluster centers and the samples belonging to the same cluster as positive samples. We conducted sufficient experiments to verify the effectiveness of our framework and the model achieves significant improvement on various image benchmarks.
A Correction-Based Dynamic Enhancement Framework towards Underwater Detection
Authors: Yanling Qiu, Qianxue Feng, Boqin Cai, Hongan Wei, Weiling Chen
Date: 2023-02-06 00:00:00
Tasks: Image Enhancement, Object, object-detection, Object Detection

To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance.
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Authors: Zachary Novack, Julian McAuley, Zachary C. Lipton, Saurabh Garg
Date: 2023-02-06 00:00:00
Tasks: Classification, image-classification, Image Classification, Prompt Engineering, zero-shot-classification, Zero-Shot Image Classification, Zero-Shot Learning

Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot classification through their ability generate embeddings for each class based on their (natural language) names. Prior work has focused on improving the accuracy of these models through prompt engineering or by incorporating a small amount of labeled downstream data (via finetuning). However, there has been little focus on improving the richness of the class names themselves, which can pose issues when class labels are coarsely-defined and are uninformative. We propose Classification with Hierarchical Label Sets (or CHiLS), an alternative strategy for zero-shot classification specifically designed for datasets with implicit semantic hierarchies. CHiLS proceeds in three steps: (i) for each class, produce a set of subclasses, using either existing label hierarchies or by querying GPT-3; (ii) perform the standard zero-shot CLIP procedure as though these subclasses were the labels of interest; (iii) map the predicted subclass back to its parent to produce the final prediction. Across numerous datasets with underlying hierarchical structure, CHiLS leads to improved accuracy in situations both with and without ground-truth hierarchical information. CHiLS is simple to implement within existing zero-shot pipelines and requires no additional training cost. Code is available at: https://github.com/acmi-lab/CHILS.
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Date: 2023-02-06 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Domain Adaptation

In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM). DoRM not only meets the criteria of high quality, large synthesis diversity, and cross-domain consistency, which were achieved by previous research in GDA, but also incorporates memory and domain association, akin to how human brains operate. Specifically, DoRM freezes the source generator and introduces new mapping and affine modules (M&A modules) to capture the attributes of the target domain during GDA. This process resembles the formation of new synapses in human brains. Consequently, a linearly combinable domain shift occurs in the style space. By incorporating multiple new M&A modules, the generator gains the capability to perform high-fidelity multi-domain and hybrid-domain generation. Moreover, to maintain cross-domain consistency more effectively, we introduce a similarity-based structure loss. This loss aligns the auto-correlation map of the target image with its corresponding auto-correlation map of the source image during training. Through extensive experiments, we demonstrate the superior performance of our DoRM and similarity-based structure loss in few-shot GDA, both quantitatively and qualitatively. The code will be available at https://github.com/wuyi2020/DoRM.
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
Authors: Dingxin Zhang, Jianhui Yu, Chaoyi Zhang, Weidong Cai
Date: 2023-02-06 00:00:00
Tasks: 3D Object Classification, Disentanglement

Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applications and harms the model performance. In this work, we introduce a novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations. Specifically, we design a siamese training module which disentangles rotation invariance and equivariance from patches defined over different scales, e.g., the local geometry and global shape, via a pair of rotations. However, our disentangled invariant feature loses the intrinsic pose information of each patch. To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales. Utilising the pose information, we propose a hierarchical module which implements intra-scale and inter-scale feature aggregation for 3D shape learning. Moreover, we introduce a pose-aware feature propagation process with the rotation-invariant relative pose information embedded. Experiments show that our disentanglement module extracts high-quality rotation-robust features and the proposed lightweight model achieves competitive results in rotated 3D object classification and part segmentation tasks. Our project page is released at: https://patchrot.github.io/.
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity
Authors: Sajid Rahim, Kourosh Sabri, Anna Ells, Alan Wassyng, Mark Lawford, Linyang Chu, Wenbo He
Date: 2023-02-06 00:00:00
Tasks: Diagnostic, Transfer Learning

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. Captured ROP Retcam images suffer from poor quality. This paper proposes the use of improved novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. Once trained and validated, the evaluations showed that these novel methods in comparison to traditional imaging processing contribute to better and in many aspects higher accuracy in classifying Plus disease, Stages of ROP and Zones in comparison to peer papers.