Abstract

Self-supervised methods have shown promising results in denoising and dehazing tasks, where the collection of the paired dataset is challenging and expensive. However, we find that these methods fail to remove the rain streaks when applied for image deraining tasks. The method’s poor per- formance is due to the explicit assumptions: (i) the distribu- tion of noise or haze is uniform and (ii) the value of a noisy or hazy pixel is independent of its neighbors. The rainy pix- els are non-uniformly distributed, and it is not necessarily dependant on its neighboring pixels. Hence, we conclude that the self-supervised method needs to have some prior knowledge about rain distribution to perform the deraining task. To provide this knowledge, we hypothesize a network trained with minimal supervision to estimate the likelihood of rainy pixels. This leads us to our proposed method called FLUID: Few Shot Self-Supervised Image Deraining. We perform extensive experiments and comparisons with existing image deraining and few-shot image-to-image translation methods on Rain 100L and DDN-SIRR datasets containing real and synthetic rainy images. In addition, we use the Rainy Cityscapes dataset to show that our method trained in a few-shot setting can improve semantic segmen- tation and object detection in rainy conditions. Our ap- proach obtains a mIoU gain of 51.20 over the current best- performing deraining method.

Abstract

Some of the issues associated with the prac- tical applications of consensus of multi-agent systems (MAS) include switching topologies, attrition and inclusion of agents from an existing network, and model uncertain- ties of agents. In this paper, a single distributed dynamic state-feedback protocol referred to as the Robust Attrition- Inclusion Distributed Dynamic (RAIDD) consensus proto- col, is synthesized for achieving the consensus of MAS with attrition and inclusion of linear time-invariant higher-order uncertain homogeneous agents and switching topologies. A state consensus problem termed as the Robust Attrition- Inclusion (RAI) consensus problem is formulated to find this RAIDD consensus protocol. To solve this RAI con- sensus problem, first, the sufficient condition for the exis- tence of the RAIDD protocol is obtained using the ν-gap metric-based simultaneous stabilization approach. Next, the RAIDD consensus protocol is attained using the Glover- McFarlane robust stabilization method if the sufficient con- dition is satisfied. The performance of this RAIDD protocol is validated by numerical simulations.

Abstract

— Sparse local feature matching is pivotal for many computer vision and robotics tasks. To improve their invariance to challenging appearance conditions and viewing angles, and hence their usefulness, existing learning-based methods have primarily focused on data augmentation-based training. In this work, we propose an alternative, complementary approach that centers on inducing bias in the model architecture itself to generate ‘rotation-specific’ features using Steerable E2-CNNs, that are then group-pooled to achieve rotation-invariant local features. We demonstrate that this high performance, rotationspecific coverage from the steerable CNNs can be expanded to all rotation angles by combining it with augmentationtrained standard CNNs which have broader coverage but are often inaccurate, thus creating a state-of-the-art rotation-robust local feature matcher. We benchmark our proposed methods against existing techniques on HPatches and a newly proposed UrbanScenes3D-Air dataset for visual place recognition. Furthermore, we present a detailed analysis of the performance effects of ensembling, robust estimation, network architecture variations, and the use of rotation priors

Abstract

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-ofthe-art CARLA simulator indicate our method reduces drift up to 76.76% compared to benchmark approaches

Abstract

Toric differential inclusions occur as key dynamical systems in the context of the Global Attractor Conjecture. We introduce the notions of minimal invariant regions and minimal globally attracting regions for toric differential inclusions. We describe a procedure for explicitly constructing the minimal invariant and minimal globally attracting regions for two-dimensional toric differential inclusions. In particular, we obtain invariant regions and globally attracting regions for two-dimensional weakly reversible or endotactic dynamical systems (even if they have time-dependent parameters).

Abstract

Infographics communicate information using a combi- nation of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly rea- son over the document layout, textual content, graphical el- ements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reason- ing and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results sug- gest that VQA on infographics—images that are designed to communicate information quickly and clearly to human brain—is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa.org

Abstract

Recent efforts in multi-domain learning for semantic seg- mentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experi- ment performed sequentially on three popular road scene segmentation datasets demonstrates that existing segmenta- tion frameworks fail at incrementally learning on a series of visually disparate geographical domains. When learning a new domain, the model catastrophically forgets previously learned knowledge. In this work, we pose the problem of multi-domain incremental learning for semantic segmenta- tion. Given a model trained on a particular geographical domain, the goal is to (i) incrementally learn a new geo- graphical domain, (ii) while retaining performance on the old domain, (iii) given that the previous domain’s dataset is not accessible. We propose a dynamic architecture that assigns universally shared, domain-invariant parameters to capture homogeneous semantic features present in all do- mains, while dedicated domain-specific parameters learn the statistics of each domain. Our novel optimization strat- egy helps achieve a good balance between retention of old knowledge (stability) and acquiring new knowledge (plas- ticity). We demonstrate the effectiveness of our proposed solution on domain incremental settings pertaining to real- world driving scenes from roads of Germany (Cityscapes), the United States (BDD100k), and India (IDD).

Abstract

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has 16 highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug 17 discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and 18 sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this 19 scenario, application of efficient state-of-the-art computational methods and modern artificial 20 intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs 21 and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a 22 powerful option to save resources and time. Structure-based drug repurposing is a popular in silico 23 repurposing approach. In this review, we discuss traditional and modern AI-based computational 24 methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. 25 Additionally, we highlight the role of generative m

Abstract

Typically, automatic syllable stress detection is posed as a supervised classification problem, for which, a classifier is trained using manually annotated (existing) syllable data and stress labels. However, in real testing scenarios, syllable data is estimated since manual annotation is not possible. Further, the estimation process could result in a mismatch between the lengths of the estimated and the existing syllable data causing no one- to-one correspondence between the estimated syllable data and the existing labels. Hence, the existing labels and estimated syllable data together cannot be used to train the classifier. This can be avoided by manually labeling the estimated syllable data, which, however, is impractical. In contrast, we, in this work, propose a method to obtain labels for estimated syllable data without using manual annotation. The proposed method considers a weighted version of the well-known Wagner–Fisher algorithm (WFA) to assign the existing labels to the estimated syllable data, where the weights are computed based on a set of three constraints defined in the proposed algorithm. Experiments on ISLE corpus show that the performance obtained on the test set for four different types of estimated syllable data are higher when the assigned labels and estimated syllable data are used for training compared to those when existing labels and existing syllable data are used. Also, the label assignment accuracy using the proposed method is found to be higher than that using a baseline scheme based on WFA.

Abstract

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG- based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.