Human Orientation Estimation under Partial Observation

Reliable human orientation estimation (HOE) is critical for autonomous agents to understand human intention and perform human-robot interaction (HRI) tasks. Great progress has been made in HOE under full observation. However, the existing methods easily make a wrong prediction under partial observation and give it an unexpectedly high probability. To solve the above problems, this study first develops a method that estimates orientation from the visible joints of a target person so that it is able to handle partial observation. Subsequently, we introduce a confidence-aware orientation estimation method, enabling more accurate orientation estimation and reasonable confidence estimation under partial observation. The effectiveness of our method is validated on both public and custom-built datasets, and it showed great accuracy and reliability improvement in partial observation scenarios. In particular, we show in real experiments that our method can benefit the robustness and consistency of the robot person following (RPF) task.

Person Re-Identification for Robot Person Following with Online Continual Learning

Robot person following (RPF) is a crucial capability in human-robot interaction (HRI) applications, allowing a robot to persistently follow a designated person. In practical RPF scenarios, the person often be occluded by other objects or people. Consequently, it is necessary to re-identify the person when he/she re-appears within the robot's field of view. Previous person re-identification (ReID) approaches to person following rely on offline-trained features and short-term experiences. Such an approach i) has a limited capacity to generalize across scenarios; and ii) often fails to re-identify the person when his re-appearance is out of the learned domain represented by the short-term experiences. Based on this observation, in this work, we propose a ReID framework for RPF that leverages long-term experiences. The experiences are maintained by a loss-guided keyframe selection strategy, to enable online continual learning of the appearance model. Our experiments demonstrate that even in the presence of severe appearance changes and distractions from visually similar people, the proposed method can still re-identify the person more accurately than the state-of-the-art methods.

HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks

Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling. Despite the well-studied adversarial attacks on Graph Neural Networks (GNN), there is few study on adversarial attacks against HGNN, which leads to a threat to the safety of HGNN applications. In this paper, we introduce HyperAttack, the first white-box adversarial attack framework against hypergraph neural networks. HyperAttack conducts a white-box structure attack by perturbing hyperedge link status towards the target node with the guidance of both gradients and integrated gradients. We evaluate HyperAttack on the widely-used Cora and PubMed datasets and three hypergraph neural networks with typical hypergraph modeling techniques. Compared to state-of-the-art white-box structural attack methods for GNN, HyperAttack achieves a 10-20X improvement in time efficiency while also increasing attack success rates by 1.3%-3.7%. The results show that HyperAttack can achieve efficient adversarial attacks that balance effectiveness and time costs.

Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .

Instance Search via Instance Level Segmentation and Feature Representation

Instance search is an interesting task as well as a challenging issue due to the lack of effective feature representation. In this paper, an instance level feature representation built upon recent fully convolutional instance-aware segmentation is proposed. The feature is ROI-pooled based on the segmented instance region. So that instances in different sizes and layouts are represented by deep feature in uniform length. This representation is further enhanced by the use of deformable ResNeXt blocks. Superior performance in terms of its distinctiveness and scalability is observed on a challenging evaluation dataset built by ourselves.