Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation

In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.

Deep Joint Source-Channel Coding for Semantic Communications

Semantic communications is considered as a promising technology for reducing the bandwidth requirements of next-generation communication systems, particularly targeting human-machine interactions. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communication seeks to ensure that only the relevant information for the underlying task is communicated to the receiver. A prominent approach to semantic communications is to model it as a joint source-channel coding (JSCC) problem. Although JSCC has been a long-standing open problem in communication and coding theory, remarkable performance gains have been shown recently over existing separate source and channel coding systems, particularly in low-latency and low-power scenarios, typically encountered in edge intelligence applications. Recent progress is thanks to the adoption of deep learning techniques for JSCC code design, which are shown to outperform the concatenation of state-of-the-art compression and channel coding schemes, each of which is a result of decades-long research efforts. In this article, we present an adaptive deep learning based JSCC (DeepJSCC) architecture for semantic communications, introduce its design principles, highlight its benefits, and outline future research challenges that lie ahead.

FBG-Based Variable-Length Estimation for Shape Sensing of Extensible Soft Robotic Manipulators

In this paper, we propose a novel variable-length estimation approach for shape sensing of extensible soft robots utilizing fiber Bragg gratings (FBGs). Shape reconstruction from FBG sensors has been increasingly developed for soft robots, while the narrow stretching range of FBG fiber makes it difficult to acquire accurate sensing results for extensible robots. Towards this limitation, we newly introduce an FBG-based length sensor by leveraging a rigid curved channel, through which FBGs are allowed to slide within the robot following its body extension/compression, hence we can search and match the FBGs with specific constant curvature in the fiber to determine the effective length. From the fusion with the above measurements, a model-free filtering technique is accordingly presented for simultaneous calibration of a variable-length model and temporally continuous length estimation of the robot, enabling its accurate shape sensing using solely FBGs. The performances of the proposed method have been experimentally evaluated on an extensible soft robot equipped with an FBG fiber in both free and unstructured environments. The results concerning dynamic accuracy and robustness of length estimation and shape sensing demonstrate the effectiveness of our approach.

Path Planning Considering Time-Varying and Uncertain Movement Speed in Multi-Robot Automatic Warehouses: Problem Formulation and Algorithm

Path planning in the multi-robot system refers to calculating a set of actions for each robot, which will move each robot to its goal without conflicting with other robots. Lately, the research topic has received significant attention for its extensive applications, such as airport ground, drone swarms, and automatic warehouses. Despite these available research results, most of the existing investigations are concerned with the cases of robots with a fixed movement speed without considering uncertainty. Therefore, in this work, we study the problem of path-planning in the multi-robot automatic warehouse context, which considers the time-varying and uncertain robots' movement speed. Specifically, the path-planning module searches a path with as few conflicts as possible for a single agent by calculating traffic cost based on customarily distributed conflict probability and combining it with the classic A* algorithm. However, this probability-based method cannot eliminate all conflicts, and speed's uncertainty will constantly cause new conflicts. As a supplement, we propose the other two modules. The conflict detection and re-planning module chooses objects requiring re-planning paths from the agents involved in different types of conflicts periodically by our designed rules. Also, at each step, the scheduling module fills up the agent's preserved queue and decides who has a higher priority when the same element is assigned to two agents simultaneously. Finally, we compare the proposed algorithm with other algorithms from academia and industry, and the results show that the proposed method is validated as the best performance.

Fourier-Net: Fast Image Registration with Band-limited Deformation

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, i.e., TransMorph, our Fourier-Net, only using 0.22$\%$ of its parameters and 6.66$\%$ of the mult-adds, achieves a 0.6\% higher Dice score and an 11.48$\times$ faster inference speed. Code is available at \url{https://github.com/xi-jia/Fourier-Net}.

CSI-PPPNet: A One-Sided Deep Learning Framework for Massive MIMO CSI Feedback

To reduce multiuser interference and maximize the spectrum efficiency in frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a deep learning (DL) based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. This way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary linear projections. In addition to the one-for-all property, in comparison to the two-sided autoencoder-based CSI feedback architecture, the one-sided framework relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. This opens new perspectives in the field of DL-based CSI feedback. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness of the proposed method.

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Medical Images

Self-supervised pre-training has become the priory choice to establish reliable models for automated recognition of massive medical images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled medical images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework towards enhanced recognition of medical images, which works in three phases by synergizing contrastive learning and generative learning models: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Extensive experiments have been performed on various public medical image datasets (e.g., CheXpert and DRIVE) against the state-of-the-art counterparts (e.g., SimCLR and MoCo), and results demonstrate that: The proposed framework statistically excels in self-supervised benchmarks, achieving 2.08, 1.23, 1.12, 0.76 and 1.38 percentage points improvements over SimCLR in AUC/Dice. The proposed framework achieves label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.

ElegantSeg: End-to-End Holistic Learning for Extra-Large Image Semantic Segmentation

This paper presents a new paradigm for Extra-large image semantic Segmentation, called ElegantSeg, that capably processes holistic extra-large image semantic segmentation (ELISS). The extremely large sizes of extra-large images (ELIs) tend to cause GPU memory exhaustion. To tackle this issue, prevailing works either follow the global-local fusion pipeline or conduct the multi-stage refinement. These methods can only process limited information at one time, and they are not able to thoroughly exploit the abundant information in ELIs. Unlike previous methods, ElegantSeg can elegantly process holistic ELISS by extending the tensor storage from GPU memory to host memory. To the best of our knowledge, it is the first time that ELISS can be performed holistically. Besides, ElegantSeg is specifically designed with three modules to utilize the characteristics of ELIs, including the multiple large kernel module for developing long-range dependency, the efficient class relation module for building holistic contextual relationships, and the boundary-aware enhancement module for obtaining complete object boundaries. ElegantSeg outperforms previous state-of-the-art on two typical ELISS datasets. We hope that ElegantSeg can open a new perspective for ELISS. The code and models will be made publicly available.