OMG-Seg: Is One Model Good Enough For All Segmentation?

In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models. We propose OMG-Seg, One Model that is Good enough to efficiently and effectively handle all the segmentation tasks, including image semantic, instance, and panoptic segmentation, as well as their video counterparts, open vocabulary settings, prompt-driven, interactive segmentation like SAM, and video object segmentation. To our knowledge, this is the first model to handle all these tasks in one model and achieve satisfactory performance. We show that OMG-Seg, a transformer-based encoder-decoder architecture with task-specific queries and outputs, can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead across various tasks and datasets. We rigorously evaluate the inter-task influences and correlations during co-training. Code and models are available at https://github.com/lxtGH/OMG-Seg.Comment: Project Page: https://lxtgh.github.io/project/omg_seg

Transformer-Based Visual Segmentation: A Survey

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several closely related settings, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmenation-With-Transformer. We will also continually monitor developments in this rapidly evolving field.Comment: Work in progress. Github: https://github.com/lxtGH/Awesome-Segmenation-With-Transforme

A token-based dynamic scheduled MAC protocol for health monitoring

Developments of wireless body area networks (WBANs) facilitate the pervasive health monitoring with mHealth applications. WBANs can support continuous health monitoring for the human body in convenience and high efficiency without any intervention. The monitoring data in health care have the characteristics of various data flows and heterogeneous data arrival rates, the transmission of which must be in timeliness and reliability, especially the burst data. Moreover, the energy-constraint nodes should be provident in energy consumption. Designing MAC protocols with high reliability and energy efficiency for WBANs is the prime consideration. In this paper, we propose a token-based two-round reservation MAC (TTR MAC) protocol based on IEEE 802.15.6 with considering the data features of health monitoring. With analyzing the characteristics of monitoring data, one-round reservation is conducted for periodic data and two-round reservation is generated adaptively for burst data to save energy. Besides, TTR MAC protocol assigns appropriate number of allocation slots to nodes in heterogeneous data arrival rates. Furthermore, a token is introduced on the basis of user priority and health severity index to indicate the transmission order of nodes with burst data, which highly decreases the average delay. In addition, a bit sequence scheduled algorithm is proposed for m-periodic (m>1) monitoring data for network capacity expansion. The simulation results show that TTR MAC protocol achieves higher energy efficiency and longer lifetime compared with IEEE 802.15.6 and other one-round reservation MAC (OR MAC) protocols for both 1-periodic and m-periodic data.info:eu-repo/semantics/publishedVersio

Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation

Video segmentation aims to segment and track every pixel in diverse scenarios accurately. In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks of video segmentation with a unified architecture. Our framework is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks. To enhance the modeling of cross-tube relationships, we propose an effective way to perform tube-level linking via attention along the queries. In addition, we introduce temporal contrastive learning to instance-wise discriminative features for tube-level association. Our approach offers flexibility and efficiency for both short and long video inputs, as the length of each subclip can be varied according to the needs of datasets or scenarios. Tube-Link outperforms existing specialized architectures by a significant margin on five video segmentation datasets. Specifically, it achieves almost 13% relative improvements on VIPSeg and 4% improvements on KITTI-STEP over the strong baseline Video K-Net. When using a ResNet50 backbone on Youtube-VIS2019 and 2021, Tube-Link boosts IDOL by 3% and 4%, respectively. Code is available at https://github.com/lxtGH/Tube-Link

TLR5 signaling enhances the proliferation of human allogeneic CD40-activated B cell induced CD4hiCD25+ regulatory T cells

Although diverse functions of different toll-like receptors (TLR) on human natural regulatory T cells have been demonstrated recently, the role of TLR-related signals on human induced regulatory T cells remain elusive. Previously our group developed an ex vivo high-efficient system in generating human alloantigen-specific CD4(hi)CD25(+) regulatory T cells from naive CD4(+)CD25(-) T cells using allogeneic CD40-activated B cells as stimulators. In this study, we investigated the role of TLR5-related signals on the generation and function of these novel CD4(hi)CD25(+) regulatory T cells. It was found that induced CD4(hi)CD25(+) regulatory T cells expressed an up-regulated level of TLR5 compared to their precursors. The blockade of TLR5 using anti-TLR5 antibodies during the co-culture decreased CD4(hi)CD25(+) regulatory T cells proliferation by induction of S phase arrest. The S phase arrest was associated with reduced ERK1/2 phosphorylation. However, TLR5 blockade did not decrease the CTLA-4, GITR and FOXP3 expressions, and the suppressive function of CD4(hi)CD25(+) regulatory T cells. In conclusion, we discovered a novel function of TLR5-related signaling in enhancing the proliferation of CD4(hi)CD25(+) regulatory T cells by promoting S phase progress but not involved in the suppressive function of human CD40-activated B cell-induced CD4(hi)CD25(+) regulatory T cells, suggesting a novel role of TLR5-related signals in the generation of induced regulatory T cells.published_or_final_versio

Caste-specific RNA-editomes in the leaf-cutting ant Acromyrmex echinatior

4 - Insect epigenomics: bridging the gap between genotype and phenotype, Oral Presentatio

Caste-specific RNA editomes in the leaf-cutting ant <i>Acromyrmex echinatior</i>

Eusocial insects have evolved the capacity to generate adults with distinct morphological, reproductive and behavioural phenotypes from the same genome. Recent studies suggest that RNA editing might enhance the diversity of gene products at the post-transcriptional level, particularly to induce functional changes in the nervous system. Using head samples from the leaf-cutting ant Acromyrmex echinatior, we compare RNA editomes across eusocial castes, identifying ca. 11,000 RNA editing sites in gynes, large workers and small workers. Those editing sites map to 800 genes functionally enriched for neurotransmission, circadian rhythm, temperature response, RNA splicing and carboxylic acid biosynthesis. Most A. echinatior editing sites are species specific, but 8–23% are conserved across ant subfamilies and likely to have been important for the evolution of eusociality in ants. The level of editing varies for the same site between castes, suggesting that RNA editing might be a general mechanism that shapes caste behaviour in ants

Transformer-Based Visual Segmentation: A Survey.

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several specific subfields, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmentation-With-Transformer