Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

Geometry and color information provided by the point clouds are both crucial for 3D scene understanding. Two pieces of information characterize the different aspects of point clouds, but existing methods lack an elaborate design for the discrimination and relevance. Hence we explore a 3D self-supervised paradigm that can better utilize the relations of point cloud information. Specifically, we propose a universal 3D scene pre-training framework via Geometry-Color Contrast (Point-GCC), which aligns geometry and color information using a Siamese network. To take care of actual application tasks, we design (i) hierarchical supervision with point-level contrast and reconstruct and object-level contrast based on the novel deep clustering module to close the gap between pre-training and downstream tasks; (ii) architecture-agnostic backbone to adapt for various downstream models. Benefiting from the object-level representation associated with downstream tasks, Point-GCC can directly evaluate model performance and the result demonstrates the effectiveness of our methods. Transfer learning results on a wide range of tasks also show consistent improvements across all datasets. e.g., new state-of-the-art object detection results on SUN RGB-D and S3DIS datasets. Codes will be released at https://github.com/Asterisci/Point-GCC

Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning

Federated learning is an important privacy-preserving multi-party learning paradigm, involving collaborative learning with others and local updating on private data. Model heterogeneity and catastrophic forgetting are two crucial challenges, which greatly limit the applicability and generalizability. This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation, facilitating the both intra-domain discriminability and inter-domain generalization. For heterogeneity issue, we leverage irrelevant unlabeled public data for communication between the heterogeneous participants. We construct cross-correlation matrix and align instance similarity distribution on both logits and feature levels, which effectively overcomes the communication barrier and improves the generalizable ability. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation, which retains inter-domain knowledge while avoiding the optimization conflict issue, fulling distilling privileged inter-domain information through depicting posterior classes relation. Considering that there is no standard benchmark for evaluating existing heterogeneous federated learning under the same setting, we present a comprehensive benchmark with extensive representative methods under four domain shift scenarios, supporting both heterogeneous and homogeneous federated settings. Empirical results demonstrate the superiority of our method and the efficiency of modules on various scenarios

Development of online education and its applicationin Shanghai Maritime University

Online teaching is becoming an important alternative approach to maritime education, which traditionally relies on face-to-face instruction, particularly during the period when the COVID-19 has had a devastating impact on the educational system worldwide. On the base of the conceptualization of online education through a literature review, this study demonstrates the case of an innovative online teaching system developed and implemented by Shanghai Maritime University (SMU) that successfully allowed some 20,000 students to resume learning despite the COVID-19 disruption. To realize large-scale online teaching, four phases of development the SMU underwent are introduced. The whole process of planning, preparation, implementation as well as evaluation is elaborated. In addition to class teaching, other major activities delivered remotely are also introduced, including short-term training programs, graduation ceremony, online career fairs, online interviews for postgraduate admission. Difficulties and challenges in shifting to the new teaching method and how SMU developed effective strategies to solve these issues are addressed. This study provides a valuable example of an online teaching system realized in a maritime institution. Furthermore, it may serve as an inspirational reference to peer maritime institutions to adopt or improve their competence of online learning systems

CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue

This paper explores interactive facial image editing via dialogue and introduces the ChatEdit benchmark dataset for evaluating image editing and conversation abilities in this context. ChatEdit is constructed from the CelebA-HQ dataset, incorporating annotated multi-turn dialogues corresponding to user edit requests on the images. The dataset is challenging, as it requires the system to dynamically track user requests, edit images, and generate appropriate responses. Accordingly, we propose three benchmark tasks: (i) user edit request tracking, (ii) image editing, and (iii) response generation. We present a novel baseline framework that integrates a dialogue module for both tracking user requests and generating responses and an image editing module for image editing. Unlike previous approaches, our framework directly tracks user edit requests from the entire dialogue history up to the current turn and modifies the original image rather than adjusting the previous turn's output, thereby reducing error accumulation and preventing attribute forgetfulness. Extensive experiments on the ChatEdit dataset underline our framework's superior performance against prior models, while also highlighting potential room for further research. We will release the code and data publicly to facilitate advancements in complex interactive facial image editing.Comment: Accepted to EMNLP 2023 (Main Conference

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. Firstly, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/WenkeHuang/MarsFL.Comment: 22 pages, 4 figure

Analysis of the influence of side wall opening on the arch structure of metro station using the PBA method

In order to meet the traffic and commercial needs, it is sometimes necessary to open the side wall of the metro station, while the current research on the mechanical properties and safety of the arch caused by the opening of the side wall of the station by pile-beam-arch (PBA) method is rarely involved. In this paper, based on the Tianhe East Station project of Guangzhou Metro Line 11 located in soft-hard uneven stratum using PBA method, the settlement law and mechanical characteristics of the arch under different side wall opening conditions is analyzed, and the influence of opening construction and opening span on the safety of arch is also further studied. The results show that the settlement caused by the opening of the side wall is mainly concentrated in the upper part of the opening area, and gradually expands around the opening area with the increase of opening span, and the maximum settlement occurs in the middle part of the arch. Opening leads to the differential settlement at both ends of the arch. With the increase in opening span, the settlement growth trend of the right side of the arch is greater than that of the left side. The opening of the side wall leads to the increase of the safety factor of the arch body and the decrease of the safety factor of the right arch foot, while the change of the safety factor of the left arch foot is not obvious, and the safety factor meets the specification requirements

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated