pwc-archive/papers-with-abstracts huggingface dataset:
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Authors: Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
Date: 2022-06-28 00:00:00
Tasks: Autonomous Driving, Motion Forecasting, Representation Learning, Self-Supervised Learning

Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning. However its potential in motion forecasting for autonomous driving has rarely been explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into motion forecasting. We first propose to investigate four novel self-supervised learning tasks for motion forecasting with theoretical rationale and quantitative and qualitative comparisons on the challenging large-scale Argoverse dataset. Secondly, we point out that our auxiliary SSL-based learning setup not only outperforms forecasting methods which use transformers, complicated fusion mechanisms and sophisticated online dense goal candidate optimization algorithms in terms of performance accuracy, but also has low inference time and architectural complexity. Lastly, we conduct several experiments to understand why SSL improves motion forecasting. Code is open-sourced at \url{https://github.com/AutoVision-cloud/SSL-Lanes}.
Visualizing and Alleviating the Effect of Radial Distortion on Camera Calibration Using Principal Lines
Authors: Jen-Hui Chuang, Hsin-Yi Chen
Date: 2022-06-28 00:00:00
Tasks: Camera Calibration

Preparing appropriate images for camera calibration is crucial to obtain accurate results. In this paper, new suggestions for preparing such data to alleviate the adverse effect of radial distortion for a calibration procedure using principal lines are developed through the investigations of: (i) identifying directions of checkerboard movements in an image which will result in maximum (and minimum) influence on the calibration results, and (ii) inspecting symmetry and monotonicity of such effect in (i) using the above principal lines. Accordingly, it is suggested that the estimation of principal point should based on linearly independent pairs of nearly parallel principal lines, with a member in each pair corresponds to a near 180-degree rotation (in the image plane) of the other. Experimental results show that more robust and consistent calibration results for the foregoing estimation can actually be obtained, compared with the renowned algebraic methods which estimate distortion parameters explicitly.
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions
Authors: Sangyun Lee, Gyojung Gu, Sunghyun Park, Seunghwan Choi, Jaegul Choo
Date: 2022-06-28 00:00:00
Tasks: Segmentation, Virtual Try-on

Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing item. To solve the task, the existing methods warp the clothing item to fit the person's body and generate the segmentation map of the person wearing the item before fusing the item with the person. However, when the warping and the segmentation generation stages operate individually without information exchange, the misalignment between the warped clothes and the segmentation map occurs, which leads to the artifacts in the final image. The information disconnection also causes excessive warping near the clothing regions occluded by the body parts, so-called pixel-squeezing artifacts. To settle the issues, we propose a novel try-on condition generator as a unified module of the two stages (i.e., warping and segmentation generation stages). A newly proposed feature fusion block in the condition generator implements the information exchange, and the condition generator does not create any misalignment or pixel-squeezing artifacts. We also introduce discriminator rejection that filters out the incorrect segmentation map predictions and assures the performance of virtual try-on frameworks. Experiments on a high-resolution dataset demonstrate that our model successfully handles the misalignment and occlusion, and significantly outperforms the baselines. Code is available at https://github.com/sangyun884/HR-VITON.
Pedestrian 3D Bounding Box Prediction
Authors: Saeed Saadatnejad, Yi Zhou Ju, Alexandre Alahi
Date: 2022-06-28 00:00:00
Tasks: Action Anticipation, Autonomous Driving, Autonomous Vehicles, Decoder, Prediction

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints prediction), we focus on 3D bounding boxes, which are reasonable estimates of humans without modeling complex motion details for autonomous vehicles. This gives the flexibility to predict in longer horizons in real-world settings. We suggest this new problem and present a simple yet effective model for pedestrians' 3D bounding box prediction. This method follows an encoder-decoder architecture based on recurrent neural networks, and our experiments show its effectiveness in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned representation has useful information to enhance the performance of other tasks, such as action anticipation. Our code is available online: https://github.com/vita-epfl/bounding-box-prediction
Flexible-Rate Learned Hierarchical Bi-Directional Video Compression With Motion Refinement and Frame-Level Bit Allocation
Authors: Eren Cetin, M. Akin Yilmaz, A. Murat Tekalp
Date: 2022-06-27 00:00:00
Tasks: Image Compression, Motion Estimation, Video Compression

This paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bi-directional video compression to further advance the state-of-the-art in learned video compression. As an improvement, we combine motion estimation and prediction modules and compress refined residual motion vectors for improved rate-distortion performance. As novel addition, we adapted the gain unit proposed for image compression to flexible-rate video compression in two ways: first, the gain unit enables a single encoder model to operate at multiple rate-distortion operating points; second, we exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate that we obtain state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation
Authors: Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jun Long, Zuhayr Asad, R. Michael Womick, Zheyu Zhu, Agnes B. Fogo, Shilin Zhao, Haichun Yang, Yuankai Huo
Date: 2022-06-27 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this paper, we propose the Omni-Seg+ network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5X to 40X scale) pathological image segmentation via a single neural network. The contribution of this paper is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from ~150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics). The official implementation is available at https://github.com/ddrrnn123/Omni-Seg.
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans
Authors: Paria Jeihouni, Omid Dehzangi, Annahita Amireskandari, Ali Rezai, Nasser M. Nasrabadi
Date: 2022-06-28 00:00:00
Tasks: Generative Adversarial Network, Segmentation, Super-Resolution

In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal layers. OCT has been identified as a non-invasive and inexpensive modality of imaging to discover potential biomarkers for the diagnosis and progress determination of neurodegenerative diseases, such as Alzheimer's Disease (AD). Current hypotheses presume the thickness of the retinal layers, which are analyzable within OCT scans, can be effective biomarkers. As a logical first step, this work concentrates on the challenging task of retinal layer segmentation and also super-resolution for higher clarity and accuracy. We propose a GAN-based segmentation model and evaluate incorporating popular networks, namely, U-Net and ResNet, in the GAN architecture with additional blocks of transposed convolution and sub-pixel convolution for the task of upscaling OCT images from low to high resolution by a factor of four. We also incorporate the Dice loss as an additional reconstruction loss term to improve the performance of this joint optimization task. Our best model configuration empirically achieved the Dice coefficient of 0.867 and mIOU of 0.765.
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
Authors: Stephen Hausler, Ming Xu, Sourav Garg, Punarjay Chakravarty, Shubham Shrivastava, Ankit Vora, Michael Milford
Date: 2022-06-28 00:00:00
Tasks: 3D geometry, Autonomous Vehicles, Camera Pose Estimation, Pose Estimation, Visual Localization

6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map. Current techniques use hierarchical pipelines and learned 2D feature extractors to improve scalability and increase performance. However, despite gains in typical recall@0.25m type metrics, these systems still have limited utility for real-world applications like autonomous vehicles because of their `worst' areas of performance - the locations where they provide insufficient recall at a certain required error tolerance. Here we investigate the utility of using `place specific configurations', where a map is segmented into a number of places, each with its own configuration for modulating the pose estimation step, in this case selecting a camera within a multi-camera system. On the Ford AV benchmark dataset, we demonstrate substantially improved worst-case localization performance compared to using off-the-shelf pipelines - minimizing the percentage of the dataset which has low recall at a certain error tolerance, as well as improved overall localization performance. Our proposed approach is particularly applicable to the crowdsharing model of autonomous vehicle deployment, where a fleet of AVs are regularly traversing a known route.
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE
Authors: Changjie Lu, Shen Zheng, ZiRui Wang, Omar Dib, Gaurav Gupta
Date: 2022-06-28 00:00:00
Tasks: Image Generation

Recently, introspective models like IntroVAE and S-IntroVAE have excelled in image generation and reconstruction tasks. The principal characteristic of introspective models is the adversarial learning of VAE, where the encoder attempts to distinguish between the real and the fake (i.e., synthesized) images. However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images. In this paper, we propose a new variation of IntroVAE called Adversarial Similarity Distance Introspective Variational Autoencoder (AS-IntroVAE). We theoretically analyze the vanishing gradient problem and construct a new Adversarial Similarity Distance (AS-Distance) using the 2-Wasserstein distance and the kernel trick. With weight annealing on AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and high-quality images. The posterior collapse problem is addressed by making per-batch attempts to transform the image so that it better fits the prior distribution in the latent space. Compared with the per-image approach, this strategy fosters more diverse distributions in the latent space, allowing our model to produce images of great diversity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image generation and reconstruction tasks.
aSTDP: A More Biologically Plausible Learning
Authors: Shiyuan Li
Date: 2022-05-22 00:00:00

Spike-timing dependent plasticity in biological neural networks has been proven to be important during biological learning process. On the other hand, artificial neural networks use a different way to learn, such as Back-Propagation or Contrastive Hebbian Learning. In this work we introduce approximate STDP, a new neural networks learning framework more similar to the biological learning process. It uses only STDP rules for supervised and unsupervised learning, every neuron distributed learn patterns and don' t need a global loss or other supervised information. We also use a numerical way to approximate the derivatives of each neuron in order to better use SDTP learning and use the derivatives to set a target for neurons to accelerate training and testing process. The framework can make predictions or generate patterns in one model without additional configuration. Finally, we verified our framework on MNIST dataset for classification and generation tasks.
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
Authors: Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
Date: 2022-06-25 00:00:00

We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed that SAFE was successful at evolving solutions within a robotic maze domain. Herein we present an investigation of SAFE's adaptation and application to multiobjective problems, wherein candidate objective functions explore different weightings of each objective. Though preliminary, the results suggest that SAFE, and the concept of coevolving solutions and objective functions, can identify a similar set of optimal multiobjective solutions without explicitly employing a Pareto front for fitness calculation and parent selection. These findings support our hypothesis that the SAFE algorithm concept can not only solve complex problems, but can adapt to the challenge of problems with multiple objectives.
The Case for RISP: A Reduced Instruction Spiking Processor
Authors: James S. Plank, ChaoHui Zheng, Bryson Gullett, Nicholas Skuda, Charles Rizzo, Catherine D. Schuman, Garrett S. Rose
Date: 2022-06-28 00:00:00

In this paper, we introduce RISP, a reduced instruction spiking processor. While most spiking neuroprocessors are based on the brain, or notions from the brain, we present the case for a spiking processor that simplifies rather than complicates. As such, it features discrete integration cycles, configurable leak, and little else. We present the computing model of RISP and highlight the benefits of its simplicity. We demonstrate how it aids in developing hand built neural networks for simple computational tasks, detail how it may be employed to simplify neural networks built with more complicated machine learning techniques, and demonstrate how it performs similarly to other spiking neurprocessors.
Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables
Authors: Manjinder Singh, Alexander E. I. Brownlee, David Cairns
Date: 2022-05-31 00:00:00

Metaheuristic search algorithms look for solutions that either maximise or minimise a set of objectives, such as cost or performance. However most real-world optimisation problems consist of nonlinear problems with complex constraints and conflicting objectives. The process by which a GA arrives at a solution remains largely unexplained to the end-user. A poorly understood solution will dent the confidence a user has in the arrived at solution. We propose that investigation of the variables that strongly influence solution quality and their relationship would be a step toward providing an explanation of the near-optimal solution presented by a metaheuristic. Through the use of four benchmark problems we use the population data generated by a Genetic Algorithm (GA) to train a surrogate model, and investigate the learning of the search space by the surrogate model. We compare what the surrogate has learned after being trained on population data generated after the first generation and contrast this with a surrogate model trained on the population data from all generations. We show that the surrogate model picks out key characteristics of the problem as it is trained on population data from each generation. Through mining the surrogate model we can build a picture of the learning process of a GA, and thus an explanation of the solution presented by the GA. The aim being to build trust and confidence in the end-user about the solution presented by the GA, and encourage adoption of the model.
Cooperative Multi-Agent Search on Endogenously-Changing Fitness Landscapes
Authors: Chin Woei Lim, Richard Allmendinger, Joshua Knowles, Ayesha Alhosani, Mercedes Bleda
Date: 2022-06-28 00:00:00

We use a multi-agent system to model how agents (representing firms) may collaborate and adapt in a business 'landscape' where some, more influential, firms are given the power to shape the landscape of other firms. The landscapes we study are based on the well-known NK model of Kauffman, with the addition of 'shapers', firms that can change the landscape's features for themselves and all other players. Our work investigates how firms that are additionally endowed with cognitive and experiential search, and the ability to form collaborations with other firms, can use these capabilities to adapt more quickly and adeptly. We find that, in a collaborative group, firms must still have a mind of their own and resist direct mimicry of stronger partners to attain better heights collectively. Larger groups and groups with more influential members generally do better, so targeted intelligent cooperation is beneficial. These conclusions are tentative, and our results show a sensitivity to landscape ruggedness and "malleability" (i.e. the capacity of the landscape to be changed by the shaper firms). Overall, our work demonstrates the potential of computer science, evolution, and machine learning to contribute to business strategy in these complex environments.
Split Two-Tower Model for Efficient and Privacy-Preserving Cross-device Federated Recommendation
Authors: Jiangcheng Qin, Baisong Liu, Xueyuan Zhang, Jiangbo Qian
Date: 2022-06-28 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated Recommendation can mitigate the systematical privacy risks of traditional recommendation since it allows the model training and online inferring without centralized user data collection. Most existing works assume that all user devices are available and adequate to participate in the Federated Learning. However, in practice, the complex recommendation models designed for accurate prediction and massive item data cause a high computation and communication cost to the resource-constrained user device, resulting in poor performance or training failure. Therefore, how to effectively compress the computation and communication overhead to achieve efficient federated recommendations across ubiquitous mobile devices remains a significant challenge. This paper introduces split learning into the two-tower recommendation models and proposes STTFedRec, a privacy-preserving and efficient cross-device federated recommendation framework. STTFedRec achieves local computation reduction by splitting the training and computation of the item model from user devices to a performance-powered server. The server with the item model provides low-dimensional item embeddings instead of raw item data to the user devices for local training and online inferring, achieving server broadcast compression. The user devices only need to perform similarity calculations with cached user embeddings to achieve efficient online inferring. We also propose an obfuscated item request strategy and multi-party circular secret sharing chain to enhance the privacy protection of model training. The experiments conducted on two public datasets demonstrate that STTFedRec improves the average computation time and communication size of the baseline models by about 40 times and 42 times in the best-case scenario with balanced recommendation accuracy.
Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses
Authors: Amifa Raj, Michael D. Ekstrand
Date: 2022-06-28 00:00:00
Tasks: Marketing

Search engines in e-commerce settings allow users to search, browse, and select items from a wide range of products available online including children's items. Children's products such as toys, books, and learning materials often have stereotype-based gender associations. Both academic research and public campaigns are working to promote stereotype-free childhood development. However, to date, e-commerce search engines have not received as much attention as physical stores, product design, or marketing as a potential channel of gender stereotypes. To fill this gap, in this paper, we study the manifestations of gender stereotypes in e-commerce sites when responding to queries related to children's products by exploring query suggestions and search results. We have three primary contributions. First, we provide an aggregated list of children's products with associated gender stereotypes from the existing body of research. Second, we provide preliminary methods for identifying and quantifying gender stereotypes in system's responses. Third, to show the importance of attending this problem, we identify the existence of gender stereotypes in query suggestions and search results across multiple e-commerce sites.