pwc-archive/papers-with-abstracts huggingface dataset:
Some theoretical results on discrete contour trees
Authors: Yuqing Song
Date: 2022-06-24 00:00:00

Contour trees have been developed to visualize or encode scalar data in imaging technologies and scientific simulations. Contours are defined on a continuous scalar field. For discrete data, a continuous function is first interpolated, where contours are then defined. In this paper we define a discrete contour tree, called the iso-tree, on a scalar graph, and discuss its properties. We show that the iso-tree model works for data of all dimensions, and develop an axiomatic system formalizing the discrete contour structures. We also report an isomorphism between iso-trees and augmented contour trees, showing that contour tree algorithms can be used to compute discrete contour trees, and vice versa.
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Authors: Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot, Julien Mairal, Xiao Xiang Zhu
Date: 2022-06-24 00:00:00
Tasks: Classification, Deep Learning, Hyperspectral Image Classification, image-classification, Image Classification, Self-Supervised Learning

Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for HSI classification, where manually labelling thousands of pixels for every scene is impractical. In this paper, we propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels. Experimental results demonstrate that this approach significantly outperforms vanilla supervised learning.
Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
Authors: Ricard Durall, Ammar Ghanim, Norman Ettrich, Janis Keuper
Date: 2022-06-24 00:00:00

Seismic processing often requires suppressing multiples that appear when collecting data. To tackle these artifacts, practitioners usually rely on Radon transform-based algorithms as post-migration gather conditioning. However, such traditional approaches are both time-consuming and parameter-dependent, making them fairly complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing its usage's complexity, and hence democratizing its applicability. We observe an excellent performance of our network when inferring complex field data, despite the fact of being solely trained on synthetics. Furthermore, extensive experiments show that our proposal can preserve the inherent characteristics of the data, avoiding undesired over-smoothed results, while removing the multiples. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters with physical events. To the best of our knowledge, this study pioneers the unboxing of neural networks for the demultiple process, helping the user to gain insights into the inside running of the network.
A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images
Authors: N. Krishna Santosh, Dr. Soubhagya Sankar Barpanda
Date: 2022-06-24 00:00:00
Tasks: Image Enhancement

In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis. Our objective is to introduce a CAD system with a fresh approach. Retinal image quality improvement is attempted in two phases. The retinal image preprocessing phase improves the brightness and contrast of the image through quantile based histogram modification. It is followed by the image enhancement phase, which involves multi scale morphological operations using image specific dynamic structuring elements for the retinal structure enrichment. Graph based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced retinal dataset. WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets. The results show our approach is superior to the existing approaches.
MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation
Authors: Yi Gu, Yuming Huang, Chengzhong Xu, Hui Kong
Date: 2022-06-24 00:00:00
Tasks: Classification, Data Augmentation, Image Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

Range-view based LiDAR segmentation methods are attractive for practical applications due to their direct inheritance from efficient 2D CNN architectures. In literature, most range-view based methods follow the per-pixel classification paradigm. Recently, in the image segmentation domain, another paradigm formulates segmentation as a mask-classification problem and has achieved remarkable performance. This raises an interesting question: can the mask-classification paradigm benefit the range-view based LiDAR segmentation and achieve better performance than the counterpart per-pixel paradigm? To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation. Along with the new paradigm, we also propose a novel data augmentation method to deal with overfitting, context-reliance, and class-imbalance problems. Extensive experiments are conducted on the SemanticKITTI benchmark. Among all published range-view based methods, our MaskRange achieves state-of-the-art performance with $66.10$ mIoU on semantic segmentation and promising results with $53.10$ PQ on panoptic segmentation with high efficiency. Our code will be released.
Contrastive Learning of Features between Images and LiDAR
Authors: Peng Jiang, Srikanth Saripalli
Date: 2022-06-24 00:00:00
Tasks: Contrastive Learning

Image and Point Clouds provide different information for robots. Finding the correspondences between data from different sensors is crucial for various tasks such as localization, mapping, and navigation. Learning-based descriptors have been developed for single sensors; there is little work on cross-modal features. This work treats learning cross-modal features as a dense contrastive learning problem. We propose a Tuple-Circle loss function for cross-modality feature learning. Furthermore, to learn good features and not lose generality, we developed a variant of widely used PointNet++ architecture for point cloud and U-Net CNN architecture for images. Moreover, we conduct experiments on a real-world dataset to show the effectiveness of our loss function and network structure. We show that our models indeed learn information from both images as well as LiDAR by visualizing the features.
Mutual Information-guided Knowledge Transfer for Novel Class Discovery
Authors: Chuyu Zhang, Chuanyang Hu, Ruijie Xu, Zhitong Gao, Qian He, Xuming He
Date: 2022-06-24 00:00:00
Tasks: Novel Class Discovery, Relation, Transfer Learning

We tackle the novel class discovery problem, aiming to discover novel classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge contained in the seen classes to unseen ones. Previous methods mostly transfer knowledge through sharing representation space or joint label space. However, they tend to neglect the class relation between seen and unseen categories, and thus the learned representations are less effective for clustering unseen classes. In this paper, we propose a principle and general method to transfer semantic knowledge between seen and unseen classes. Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge. To validate the effectiveness and generalization of our method, we conduct extensive experiments both on novel class discovery and general novel class discovery settings. Our results show that the proposed method outperforms previous SOTA by a significant margin on several benchmarks.
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
Authors: Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, Xin Tong
Date: 2022-06-24 00:00:00
Tasks: 3D Generation, 3D Shape Generation, 3D Shape Representation

We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shapes and a shape collection. We extend StyleGAN2 to 3D generation and utilize the implicit signed distance function (SDF) as the 3D shape representation, and introduce two novel global and local shape discriminators that distinguish real and fake SDF values and gradients to significantly improve shape geometry and visual quality. We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes. Experiments on shape generation demonstrate the superior performance of SDF-StyleGAN over the state-of-the-art. We further demonstrate the efficacy of SDF-StyleGAN in various tasks based on GAN inversion, including shape reconstruction, shape completion from partial point clouds, single-view image-based shape generation, and shape style editing. Extensive ablation studies justify the efficacy of our framework design. Our code and trained models are available at https://github.com/Zhengxinyang/SDF-StyleGAN.
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution
Authors: Bo Yan, Leilei Cao, Fengliang Qi, Hongbin Wang
Date: 2022-06-24 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

In recent years, the Thermal Image Super-Resolution (TISR) problem has become an attractive research topic. TISR would been used in a wide range of fields, including military, medical, agricultural and animal ecology. Due to the success of PBVS-2020 and PBVS-2021 workshop challenge, the result of TISR keeps improving and attracts more researchers to sign up for PBVS-2022 challenge. In this paper, we will introduce the technical details of our submission to PBVS-2022 challenge designing a Bilateral Network with Channel Splitting Network and Transformer(BN-CSNT) to tackle the TISR problem. Firstly, we designed a context branch based on channel splitting network with transformer to obtain sufficient context information. Secondly, we designed a spatial branch with shallow transformer to extract low level features which can preserve the spatial information. Finally, for the context branch in order to fuse the features from channel splitting network and transformer, we proposed an attention refinement module, and then features from context branch and spatial branch are fused by proposed feature fusion module. The proposed method can achieve PSNR=33.64, SSIM=0.9263 for x4 and PSNR=21.08, SSIM=0.7803 for x2 in the PBVS-2022 challenge test dataset.
Protecting President Zelenskyy against Deep Fakes
Authors: Matyáš Boháček, Hany Farid
Date: 2022-06-24 00:00:00

The 2022 Russian invasion of Ukraine is being fought on two fronts: a brutal ground war and a duplicitous disinformation campaign designed to conceal and justify Russia's actions. This campaign includes at least one example of a deep-fake video purportedly showing Ukrainian President Zelenskyy admitting defeat and surrendering. In anticipation of future attacks of this form, we describe a facial and gestural behavioral model that captures distinctive characteristics of Zelenskyy's speaking style. Trained on over eight hours of authentic video from four different settings, we show that this behavioral model can distinguish Zelenskyy from deep-fake imposters.This model can play an important role -- particularly during the fog of war -- in distinguishing the real from the fake.
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation
Authors: Leilei Cao, Zhuang Li, Bo Yan, Feng Zhang, Fengliang Qi, Yuchen Hu, Hongbin Wang
Date: 2022-06-24 00:00:00
Tasks: Object, object-detection, Object Detection, Referring Video Object Segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames. Due to the requirement of understanding cross-modal semantics within individual instances, this task is more challenging than the traditional semi-supervised video object segmentation where the ground truth object masks in the first frame are given. With the great achievement of Transformer in object detection and object segmentation, RVOS has been made remarkable progress where ReferFormer achieved the state-of-the-art performance. In this work, based on the strong baseline framework--ReferFormer, we propose several tricks to boost further, including cyclical learning rates, semi-supervised approach, and test-time augmentation inference. The improved ReferFormer ranks 2nd place on CVPR2022 Referring Youtube-VOS Challenge.
UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields
Authors: Abiramy Kuganesan, Shih-Yang Su, James J. Little, Helge Rhodin
Date: 2022-06-23 00:00:00
Tasks: Density Estimation, NeRF, Novel View Synthesis

Neural Radiance Fields (NeRFs) increase reconstruction detail for novel view synthesis and scene reconstruction, with applications ranging from large static scenes to dynamic human motion. However, the increased resolution and model-free nature of such neural fields come at the cost of high training times and excessive memory requirements. Recent advances improve the inference time by using complementary data structures yet these methods are ill-suited for dynamic scenes and often increase memory consumption. Little has been done to reduce the resources required at training time. We propose a method to exploit the redundancy of NeRF's sample-based computations by partially sharing evaluations across neighboring sample points. Our UNeRF architecture is inspired by the UNet, where spatial resolution is reduced in the middle of the network and information is shared between adjacent samples. Although this change violates the strict and conscious separation of view-dependent appearance and view-independent density estimation in the NeRF method, we show that it improves novel view synthesis. We also introduce an alternative subsampling strategy which shares computation while minimizing any violation of view invariance. UNeRF is a plug-in module for the original NeRF network. Our major contributions include reduction of the memory footprint, improved accuracy, and reduced amortized processing time both during training and inference. With only weak assumptions on locality, we achieve improved resource utilization on a variety of neural radiance fields tasks. We demonstrate applications to the novel view synthesis of static scenes as well as dynamic human shape and motion.
TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
Authors: Adam Shephard, Mostafa Jahanifar, Ruoyu Wang, Muhammad Dawood, Simon Graham, Kastytis Sidlauskas, Syed Ali Khurram, Nasir Rajpoot, Shan E Ahmed Raza
Date: 2022-06-23 00:00:00
Tasks: Prognosis

The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before localising the tumor bulk region for TILs detection. Finally, we use these outputs to generate a TILs score for each case. On preliminary testing, our approach achieved a tumor-stroma weighted Dice score of 0.791 and a FROC score of 0.572 for lymphocytic detection. For predicting survival, our model achieved a C-index of 0.719. These results achieved first place across the preliminary testing leaderboards of the TiGER challenge.
Towards Galaxy Foundation Models with Hybrid Contrastive Learning
Authors: Mike Walmsley, Inigo Val Slijepcevic, Micah Bowles, Anna M. M. Scaife
Date: 2022-06-23 00:00:00
Tasks: Contrastive Learning

New astronomical tasks are often related to earlier tasks for which labels have already been collected. We adapt the contrastive framework BYOL to leverage those labels as a pretraining task while also enforcing augmentation invariance. For large-scale pretraining, we introduce GZ-Evo v0.1, a set of 96.5M volunteer responses for 552k galaxy images plus a further 1.34M comparable unlabelled galaxies. Most of the 206 GZ-Evo answers are unknown for any given galaxy, and so our pretraining task uses a Dirichlet loss that naturally handles unknown answers. GZ-Evo pretraining, with or without hybrid learning, improves on direct training even with plentiful downstream labels (+4% accuracy with 44k labels). Our hybrid pretraining/contrastive method further improves downstream accuracy vs. pretraining or contrastive learning, especially in the low-label transfer regime (+6% accuracy with 750 labels).
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models
Authors: Zhicheng Yang, Jui-Hsin Lai, Jun Zhou, Hang Zhou, Chen Du, Zhongcheng Lai
Date: 2022-06-23 00:00:00
Tasks: Data Augmentation

The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle
Authors: Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang
Date: 2022-06-23 00:00:00

Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. Recent studies demonstrate that examples are not equally important to the final robustness of models during AT, that is, the so-called hard examples that can be attacked easily exhibit more influence than robust examples on the final robustness. Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model. However, defining effective heuristics to search for hard examples is still difficult. In this article, inspired by the information bottleneck (IB) principle, we uncover that an example with high mutual information of the input and its associated latent representation is more likely to be attacked. Based on this observation, we propose a novel and effective adversarial training method (InfoAT). InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models. Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.
Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Date: 2022-06-24 00:00:00
Tasks: Time Series, Time Series Analysis

Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations. Here we propose a convolutional neural network model to predict the stability of hierarchical triples by looking at their evolution during the first $5 \times 10^5$ inner binary orbits. We employ the regularized few-body code TSUNAMI to simulate $5\times 10^6$ hierarchical triples, from which we generate a large training and test dataset. We develop twelve different network configurations that use different combinations of the triples' orbital elements and compare their performances. Our best model uses 6 time-series, namely, the semimajor axes ratio, the inner and outer eccentricities, the mutual inclination and the arguments of pericenter. This model achieves an area under the curve of over $95\%$ and informs of the relevant parameters to study triple systems stability. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
Authors: Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren
Date: 2022-06-24 00:00:00
Tasks: Recommendation Systems

Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. In such attacks, an adversary aims to infer whether a user's data is used to train the target recommender. To achieve this, previous work has used a shadow recommender to derive training data for the attack model, and then predicts the membership by calculating difference vectors between users' historical interactions and recommended items. State-of-the-art methods face two challenging problems: (1) training data for the attack model is biased due to the gap between shadow and target recommenders, and (2) hidden states in recommenders are not observational, resulting in inaccurate estimations of difference vectors. To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model. To mitigate the gap between recommenders, a variational auto-encoder (VAE) based disentangled encoder is devised to identify recommender invariant and specific features. To reduce the estimation bias, we design a weight estimator, assigning a truth-level score for each difference vector to indicate estimation accuracy. We evaluate DL-MIA against both general recommenders and sequential recommenders on three real-world datasets. Experimental results show that DL-MIA effectively alleviates training and estimation biases simultaneously, and achieves state-of-the-art attack performance.