Abstract

Crowdsourcing is an effective method to collect data by employingdistributed human population. Researchers introduce Peer-BasedMechanisms (PBMs) in crowdsourcing settings to incentivize agentsto report accurately. We observe that with PBMs, crowdsourcingsystems may not be fair. Unfair rewards for the agents may discour-age participation. This work aims to build a general frameworkthat assures fairness for PBMs in a temporal setting, i.e., wherereports are time-sensitive. Towards this, we introduce two notionsof fairness for PBMs, namely𝛾-fairnessandqualitative fairness. Tosatisfy these notions, our framework provides trustworthy agentswith additional chances of pairing. We introduce Temporal Reputa-tion Model (TERM) to quantify agents’ trustworthiness across tasks.Having TERM, we present our iterative framework, REFORM, thatcan adopt the reward scheme of any existing PBM. We demonstrateREFORM’s significance by deploying the framework with RPTSC’sreward scheme and prove that REFORM with RPTSC considerablyimproves fairness; while incentivizing truthful and early reports.

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

Automated generation and (user) authoring of the realistic virtual terrain is most sought for by the multimedia applications like VR models and gaming. The most common representation adopted for terrain is Digital Elevation Model (DEM). Existing terrain authoring and modelling techniques have addressed some of these and can be broadly categorised as: procedural modeling, simulation method, and example-based methods. In this paper, we propose a novel realistic terrain authoring framework powered by a combination of VAE and generative conditional GAN model. Our framework is an example-based method that attempt to overcome the limitations of existing methods by learning a latent space from real world terrain dataset. This latent space allows us to generate multiple variants of terrain from a single input as well as interpolate between terrains, while keeping the generated terrains close to real world data distribution. We also developed an interactive tool, that lets the user generate diverse terrains with minimalist inputs. We perform thorough qualitative and quantitative analysis and provide comparison with other SOTA methods. We intend to release our code/tool to academic community.

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

Table structure recognition is necessary for a compre- hensive understanding of documents. Tables in unstruc- tured business documents are tough to parse due to the high diversity of layouts, varying alignments of contents, and the presence of empty cells. The problem is particularly dif- ficult because of challenges in identifying individual cells using visual or linguistic contexts or both. Accurate detec- tion of table cells (including empty cells) simplifies struc- ture extraction and hence, it becomes the prime focus of our work. We propose a novel object-detection-based deep model that captures the inherent alignments of cells within tables and is fine-tuned for fast optimization. Despite ac- curate detection of cells, recognizing structures for dense tables may still be challenging because of difficulties in cap- turing long-range row/column dependencies in presence of multi-row/column spanning cells. Therefore, we also aim to improve structure recognition by deducing a novel rec- tilinear graph-based formulation. From a semantics per- spective, we highlight the significance of empty cells in a table. To take these cells into account, we suggest an en- hancement to a popular evaluation criterion. Finally, we introduce a modestly sized evaluation dataset with an anno- tation style inspired by human cognition to encourage new approaches to the problem. Our framework improves the previous state-of-the-art performance by a 2.7% average F1-score on benchmark datasets.

Abstract

The insulin hormone is stored in the hexameric form and dissociates to the dimeric form and finally to the active monomeric form. When insulin secretion is impaired, thereby affecting various metabolic processes, as a final response, insulin analogs are subcutaneously injected before meals to facilitate glucose metabolism. Depending on the molecular details, analogs are rapid or slow acting based on the dissociation rate of the dimer to monomer. Insulin aspart is a recombinant human insulin analog, acting faster than regular human insulin. Despite its practical and elementary importance, the process of insulin aspart dimer dissociation is relatively unknown. Here, we combined molecular dynamics simulations and umbrella sampling to characterize the energetic and structural features of dissociation of the insulin aspart dimer. Like previous studies on human insulin (another well studied analog), insulin aspart can also display a wide spectrum of pathways for dimer dissociation from dissociation happening without a major change in the monomer structure to dissociation that is coupled with unfolding of a protein. Additionally, water plays a vital role in the dissociation of the insulin aspart by stabilizing the monomers in the dissociated state. Our study shows the molecular details, such as the variation in the structure and orientation and conformational changes along the minimum energy pathways in the process of dissociation of the insulin aspart dimer.