pwc-archive/papers-with-abstracts huggingface dataset:
Globally Optimal Event-Based Divergence Estimation for Ventral Landing
Authors: Sofia McLeod, Gabriele Meoni, Dario Izzo, Anne Mergy, Daqi Liu, Yasir Latif, Ian Reid, Tat-Jun Chin
Date: 2022-09-27 00:00:00
Tasks: GPU

Event sensing is a major component in bio-inspired flight guidance and control systems. We explore the usage of event cameras for predicting time-to-contact (TTC) with the surface during ventral landing. This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing. Our core contributions are a novel contrast maximisation formulation for event-based divergence estimation, and a branch-and-bound algorithm to exactly maximise contrast and find the optimal divergence value. GPU acceleration is conducted to speed up the global algorithm. Another contribution is a new dataset containing real event streams from ventral landing that was employed to test and benchmark our method. Owing to global optimisation, our algorithm is much more capable at recovering the true divergence, compared to other heuristic divergence estimators or event-based optic flow methods. With GPU acceleration, our method also achieves competitive runtimes.
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Authors: Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen Lennerz, Stephen Yip, Hossein Farahani, Ali Bashashati
Date: 2022-09-27 00:00:00
Tasks: Color Normalization

Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.
Ki-67 Index Measurement in Breast Cancer Using Digital Image Analysis
Authors: Hsiang-Wei Huang, Wen-Tsung Huang, Hsun-Heng Tsai
Date: 2022-09-27 00:00:00
Tasks: Binarization

Ki-67 is a nuclear protein that can be produced during cell proliferation. The Ki67 index is a valuable prognostic variable in several kinds of cancer. In breast cancer, the index is even routinely checked in many patients. Currently, pathologists use the immunohistochemistry method to calculate the percentage of Ki-67 positive malignant cells as Ki-67 index. The higher score usually means more aggressive tumor behavior. In clinical practice, the measurement of Ki-67 index relies on visual identifying method and manual counting. However, visual and manual assessment method is timeconsuming and leads to poor reproducibility because of different scoring standards or limited tumor area under assessment. Here, we use digital image processing technics including image binarization and image morphological operations to create a digital image analysis method to interpretate Ki-67 index. Then, 10 breast cancer specimens are used as validation with high accuracy (correlation efficiency r = 0.95127). With the assistance of digital image analysis, pathologists can interpretate the Ki67 index more efficiently, precisely with excellent reproducibility.
Observation Centric and Central Distance Recovery on Sports Player Tracking
Authors: Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee
Date: 2022-09-27 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking

Multi-Object Tracking over humans has improved rapidly with the development of object detection and re-identification. However, multi-actor tracking over humans with similar appearance and nonlinear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-players tracking over the sports field, because the players in the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motionbased tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experiments result on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset demonstrate the effectiveness of our method, which achieves a HOTA of 73.968, ranking 3rd place on the 2022 Sportsmot workshop final leaderboard.
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
Authors: Brian Moser, Federico Raue, Stanislav Frolov, Jörn Hees, Sebastian Palacio, Andreas Dengel
Date: 2022-09-27 00:00:00
Tasks: Neural Architecture Search, Super-Resolution

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances, and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We present a critical discussion on contemporary strategies used in SR, and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latests evaluation techniques. We also include several visualizations for the models and methods throughout each chapter in order to facilitate a global understanding of the trends in the field. This review is ultimately aimed at helping researchers to push the boundaries of DL applied to SR.
Spatio-Temporal Relation Learning for Video Anomaly Detection
Authors: Hui Lv, Zhen Cui, Biao Wang, Jian Yang
Date: 2022-09-27 00:00:00
Tasks: Anomaly Detection, Graph Embedding, Knowledge Graph Embedding, Object, Optical Flow Estimation, Relation, Representation Learning, Video Anomaly Detection

Anomaly identification is highly dependent on the relationship between the object and the scene, as different/same object actions in same/different scenes may lead to various degrees of normality and anomaly. Therefore, object-scene relation actually plays a crucial role in anomaly detection but is inadequately explored in previous works. In this paper, we propose a Spatial-Temporal Relation Learning (STRL) framework to tackle the video anomaly detection task. First, considering dynamic characteristics of the objects as well as scene areas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit spatial and temporal evolution patterns for representation learning. For better pattern extraction, two decoding branches are designed in the STAE module, i.e. an appearance branch capturing spatial cues by directly predicting the next frame, and a motion branch focusing on modeling the dynamics via optical flow prediction. Then, to well concretize the object-scene relation, a Relation Learning (RL) module is devised to analyze and summarize the normal relations by introducing the Knowledge Graph Embedding methodology. Specifically in this process, the plausibility of object-scene relation is measured by jointly modeling object/scene features and optimizable object-scene relation maps. Extensive experiments are conducted on three public datasets, and the superior performance over the state-of-the-art methods demonstrates the effectiveness of our method.
FG-UAP: Feature-Gathering Universal Adversarial Perturbation
Authors: Zhixing Ye, Xinwen Cheng, Xiaolin Huang
Date: 2022-09-27 00:00:00

Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations, whether such perturbations are dependent or independent of images. The latter one, called Universal Adversarial Perturbation (UAP), is very attractive for model robustness analysis, since its independence of input reveals the intrinsic characteristics of the model. Relatively, another interesting observation is Neural Collapse (NC), which means the feature variability may collapse during the terminal phase of training. Motivated by this, we propose to generate UAP by attacking the layer where NC phenomenon happens. Because of NC, the proposed attack could gather all the natural images' features to its surrounding, which is hence called Feature-Gathering UAP (FG-UAP). We evaluate the effectiveness our proposed algorithm on abundant experiments, including untargeted and targeted universal attacks, attacks under limited dataset, and transfer-based black-box attacks among different architectures including Vision Transformers, which are believed to be more robust. Furthermore, we investigate FG-UAP in the view of NC by analyzing the labels and extracted features of adversarial examples, finding that collapse phenomenon becomes stronger after the model is corrupted. The code will be released when the paper is accepted.
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
Authors: Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Date: 2022-09-27 00:00:00

The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains. However, a vast number of deep architectures are only able to formulate predictions without an associated uncertainty. In this paper, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass. We couple our methods with a tunable rejection-based approach that employs only the fraction of the dataset that the model is able to classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from Alzheimer's Disease patients, where we tackle discrimination of patients from healthy controls based on morphometric images only. We demonstrate how combining the estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select cases to be recommended for manual evaluation based on excessive uncertainty. We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with) can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix
Authors: Kelum Gajamannage, Yonggi Park, S. M. Mallikarjunaiah, Sunil Mathur
Date: 2022-09-27 00:00:00
Tasks: Denoising, Image Denoising, Object Tracking

With the advent of sophisticated cameras, the urge to capture high-quality images has grown enormous. However, the noise contamination of the images results in substandard expectations among the people; thus, image denoising is an essential pre-processing step. While the algebraic image processing frameworks are sometimes inefficient for this denoising task as they may require processing of matrices of order equivalent to some power of the order of the original image, the neural network image processing frameworks are sometimes not robust as they require a lot of similar training samples. Thus, here we present a manifold-based noise filtering method that mainly exploits a few prominent singular vectors of the geodesics' Gramian matrix. Especially, the framework partitions an image, say that of size $n \times n$, into $n^2$ overlapping patches of known size such that one patch is centered at each pixel. Then, the prominent singular vectors, of the Gramian matrix of size $n^2 \times n^2$ of the geodesic distances computed over the patch space, are utilized to denoise the image. Here, the prominent singular vectors are revealed by efficient, but diverse, approximation techniques, rather than explicitly computing them using frameworks like Singular Value Decomposition (SVD) which encounters $\mathcal{O}(n^6)$ operations. Finally, we compare both computational time and the noise filtration performance of the proposed denoising algorithm with and without singular vector approximation techniques.
WaterNeRF: Neural Radiance Fields for Underwater Scenes
Authors: Advaith Venkatramanan Sethuraman, Manikandasriram Srinivasan Ramanagopal, Katherine A. Skinner
Date: 2022-09-27 00:00:00
Tasks: 3D Reconstruction, Depth Estimation

Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitoring. However, water column effects, such as attenuation and backscattering, drastically change the color and quality of imagery captured underwater. Due to varying water conditions and range-dependency of these effects, restoring underwater imagery is a challenging problem. This impacts downstream perception tasks including depth estimation and 3D reconstruction. In this paper, we advance state-of-the-art in neural radiance fields (NeRFs) to enable physics-informed dense depth estimation and color correction. Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation, leading to a hybrid data-driven and model-based solution. After determining the scene structure and radiance field, we can produce novel views of degraded as well as corrected underwater images, along with dense depth of the scene. We evaluate the proposed method qualitatively and quantitatively on a real underwater dataset.
Diversified Dynamic Routing for Vision Tasks
Authors: Botos Csaba, Adel Bibi, Yanwei Li, Philip Torr, Ser-Nam Lim
Date: 2022-09-26 00:00:00
Tasks: Instance Segmentation, Mixture-of-Experts, object-detection, Object Detection, Semantic Segmentation

Deep learning models for vision tasks are trained on large datasets under the assumption that there exists a universal representation that can be used to make predictions for all samples. Whereas high complexity models are proven to be capable of learning such representations, a mixture of experts trained on specific subsets of the data can infer the labels more efficiently. However using mixture of experts poses two new problems, namely (i) assigning the correct expert at inference time when a new unseen sample is presented. (ii) Finding the optimal partitioning of the training data, such that the experts rely the least on common features. In Dynamic Routing (DR) a novel architecture is proposed where each layer is composed of a set of experts, however without addressing the two challenges we demonstrate that the model reverts to using the same subset of experts. In our method, Diversified Dynamic Routing (DivDR) the model is explicitly trained to solve the challenge of finding relevant partitioning of the data and assigning the correct experts in an unsupervised approach. We conduct several experiments on semantic segmentation on Cityscapes and object detection and instance segmentation on MS-COCO showing improved performance over several baselines.
Effective Invertible Arbitrary Image Rescaling
Authors: Zhihong Pan, Baopu Li, Dongliang He, Wenhao Wu, Errui Ding
Date: 2022-09-26 00:00:00
Tasks: Image Rescaling, Image Super-Resolution, Super-Resolution

Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have also been proposed to restore SR images with arbitrary scale factors, including asymmetric ones where images are resized to different scales along horizontal and vertical directions. Though most models are only optimized for the unidirectional upscaling task while assuming a predefined downscaling kernel for low-resolution (LR) inputs, recent models based on Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. However, limited by the INN architecture, it is constrained to fixed integer scale factors and requires one model for each scale. Without increasing model complexity, a simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. Using innovative components like position-aware scale encoding and preemptive channel splitting, the network is optimized to convert the non-invertible rescaling cycle to an effectively invertible process. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs. It is also demonstrated to perform well on tests with asymmetric scales using the same network architecture.
Totems: Physical Objects for Verifying Visual Integrity
Authors: Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba
Date: 2022-09-26 00:00:00
Tasks: Image Forensics

We introduce a new approach to image forensics: placing physical refractive objects, which we call totems, into a scene so as to protect any photograph taken of that scene. Totems bend and redirect light rays, thus providing multiple, albeit distorted, views of the scene within a single image. A defender can use these distorted totem pixels to detect if an image has been manipulated. Our approach unscrambles the light rays passing through the totems by estimating their positions in the scene and using their known geometric and material properties. To verify a totem-protected image, we detect inconsistencies between the scene reconstructed from totem viewpoints and the scene's appearance from the camera viewpoint. Such an approach makes the adversarial manipulation task more difficult, as the adversary must modify both the totem and image pixels in a geometrically consistent manner without knowing the physical properties of the totem. Unlike prior learning-based approaches, our method does not require training on datasets of specific manipulations, and instead uses physical properties of the scene and camera to solve the forensics problem.
USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations
Authors: Sophie Ostmeier, Brian Axelrod, Jeroen Bertels, Fabian Isensee, Maarten G. Lansberg, Soren Christensen, Gregory W. Albers, Li-Jia Li, Jeremy J. Heit
Date: 2022-09-26 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics fail to measure the impact of this mismatch, especially for clinical data sets that include low signal pathologies, a difficult segmentation task, and uncertain, small, or empty reference annotations. This limitation may result in ineffective research of machine learning practitioners in designing and optimizing models. Dimensions of evaluating clinical value include consideration of the uncertainty of reference annotations, independence from reference annotation volume size, and evaluation of classification of empty reference annotations. We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify metrics with clinical value. We compare to a public benchmark data set (BraTS 2019) with a high-signal pathology and certain, larger, and no empty reference annotations. We may show machine learning practitioners, how uncertain, small, or empty reference annotations require a rethinking of the evaluation and optimizing procedures. The evaluation code was released to encourage further analysis of this topic. https://github.com/SophieOstmeier/UncertainSmallEmpty.git
Habitat classification from satellite observations with sparse annotations
Authors: Mikko Impiö, Pekka Härmä, Anna Tammilehto, Saku Anttila, Jenni Raitoharju
Date: 2022-09-26 00:00:00
Tasks: Classification, Land Cover Classification, Segmentation, Transfer Learning

Remote sensing benefits habitat conservation by making monitoring of large areas easier compared to field surveying especially if the remote sensed data can be automatically analyzed. An important aspect of monitoring is classifying and mapping habitat types present in the monitored area. Automatic classification is a difficult task, as classes have fine-grained differences and their distributions are long-tailed and unbalanced. Usually training data used for automatic land cover classification relies on fully annotated segmentation maps, annotated from remote sensed imagery to a fairly high-level taxonomy, i.e., classes such as forest, farmland, or urban area. A challenge with automatic habitat classification is that reliable data annotation requires field-surveys. Therefore, full segmentation maps are expensive to produce, and training data is often sparse, point-like, and limited to areas accessible by foot. Methods for utilizing these limited data more efficiently are needed. We address these problems by proposing a method for habitat classification and mapping, and apply this method to classify the entire northern Finnish Lapland area into Natura2000 classes. The method is characterized by using finely-grained, sparse, single-pixel annotations collected from the field, combined with large amounts of unannotated data to produce segmentation maps. Supervised, unsupervised and semi-supervised methods are compared, and the benefits of transfer learning from a larger out-of-domain dataset are demonstrated. We propose a \ac{CNN} biased towards center pixel classification ensembled with a random forest classifier, that produces higher quality classifications than the models themselves alone. We show that cropping augmentations, test-time augmentation and semi-supervised learning can help classification even further.