HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers

We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasping of objects. We create training and evaluation environments for the receiver with standardized protocols and metrics. We analyze the performance of a set of baselines and show a correlation with a real-world evaluation. Code is open sourced at https://handover-sim.github.io.

Transformer Tracking with Cyclic Shifting Window Attention

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks.

Detecting Topology Attacks against Graph Neural Networks

Graph neural networks (GNNs) have been widely used in many real applications, and recent studies have revealed their vulnerabilities against topology attacks. To address this issue, existing efforts have mainly been dedicated to improving the robustness of GNNs, while little attention has been paid to the detection of such attacks. In this work, we study the victim node detection problem under topology attacks against GNNs. Our approach is built upon the key observation rooted in the intrinsic message passing nature of GNNs. That is, the neighborhood of a victim node tends to have two competing group forces, pushing the node classification results towards the original label and the targeted label, respectively. Based on this observation, we propose to detect victim nodes by deliberately designing an effective measurement of the neighborhood variance for each node. Extensive experimental results on four real-world datasets and five existing topology attacks show the effectiveness and efficiency of the proposed detection approach.

CorrGAN: Input Transformation Technique Against Natural Corruptions

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning

Tumor mutational burden (TMB) is a potential genomic biomarker that can help identify patients who will benefit from immunotherapy across a variety of cancers. We included whole slide images (WSIs) of 3228 diagnostic slides from the Cancer Genome Atlas and 531 WSIs from the Clinical Proteomic Tumor Analysis Consortium for the development and verification of a pan-cancer TMB prediction model (PC-TMB). We proposed a multiscale weakly-supervised deep learning framework for predicting TMB of seven types of tumors based only on routinely used hematoxylin-eosin (H&E)-stained WSIs. PC-TMB achieved a mean area under curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which was superior to the best single-scale model. In comparison with the state-of-the-art TMB prediction model from previous publications, our multiscale model achieved better performance over previously reported models. In addition, the improvements of PC-TMB over the single-tumor models were also confirmed by the ablation tests on 10x magnification. The PC-TMB algorithm also exhibited good generalization on external validation cohort with AUC of 0.732 (0.683-0.761). PC-TMB possessed a comparable survival-risk stratification performance to the TMB measured by whole exome sequencing, but with low cost and being time-efficient for providing a prognostic biomarker of multiple solid tumors. Moreover, spatial heterogeneity of TMB within tumors was also identified through our PC-TMB, which might enable image-based screening for molecular biomarkers with spatial variation and potential exploring for genotype-spatial heterogeneity relationships.

Model Predictive Control for Fluid Human-to-Robot Handovers

Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handovers of unknown objects by using learning-based grasp generators. However, how to responsively generate smooth motions to take an object from a human is still an open question. Specifically, planning motions that take human comfort into account is not a part of the human-robot handover process in most prior works. In this paper, we propose to generate smooth motions via an efficient model-predictive control (MPC) framework that integrates perception and complex domain-specific constraints into the optimization problem. We introduce a learning-based grasp reachability model to select candidate grasps which maximize the robot's manipulability, giving it more freedom to satisfy these constraints. Finally, we integrate a neural net force/torque classifier that detects contact events from noisy data. We conducted human-to-robot handover experiments on a diverse set of objects with several users (N=4) and performed a systematic evaluation of each module. The study shows that the users preferred our MPC approach over the baseline system by a large margin. More results and videos are available at https://sites.google.com/nvidia.com/mpc-for-handover.

NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models

Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work focuses on improving NICG model accuracy while efficiency is less explored. However, many real-world applications require real-time feedback, which highly relies on the efficiency of NICG models. Recent research observed that the efficiency of NICG models could vary for different inputs. This observation brings in a new attack surface of NICG models, i.e., An adversary might be able to slightly change inputs to cause the NICG models to consume more computational resources. To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models. Our experimental results show that NICGSlowDown can generate images with human-unnoticeable perturbations that will increase the NICG model latency up to 483.86%. We hope this research could raise the community's concern about the efficiency robustness of NICG models.

NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation

Existing neural reconstruction schemes such as Neural Radiance Field (NeRF) are largely focused on modeling opaque objects. We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front. Unlike prior arts that treat the reconstruction target as a single layer of the surface, NeReF is specifically formulated to recover a volumetric normal field with its corresponding density field. A query ray will be refracted by NeReF according to its accumulated refractive point and normal, and we employ the correspondences and uniqueness of refracted ray for NeReF optimization. We show NeReF, as a global optimization scheme, can more robustly tackle refraction distortions detrimental to traditional methods for correspondence matching. Furthermore, the continuous NeReF representation of wavefront enables view synthesis as well as normal integration. We validate our approach on both synthetic and real data and show it is particularly suitable for sparse multi-view acquisition. We hence build a small light field array and experiment on various surface shapes to demonstrate high fidelity NeReF reconstruction.

Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on a disarray of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. Two key components are well-structured latent spaces due to dense temporal samplings from videos and explicit facial expression controls to regulate the latent spaces. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with an ultra-fast video camera to obtain raw 3D facial assets. We then model the facial expression, geometry and physically-based textures using separate VAEs with a global MLP-based expression mapping across the latent spaces, to preserve characteristics across respective attributes while maintaining explicit controls over geometry and texture. We also introduce to model the delta information as wrinkle maps for physically-base textures, achieving high-quality rendering of dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our neural asset along with fast adaptation schemes can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangle strategy by providing promising physically-based editing results like geometry and material editing or winkle transfer with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.