VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search

Text-based Person Search (TBPS) aims to retrieve images of target pedestrian indicated by textual descriptions. It is essential for TBPS to extract fine-grained local features and align them crossing modality. Existing methods utilize external tools or heavy cross-modal interaction to achieve explicit alignment of cross-modal fine-grained features, which is inefficient and time-consuming. In this work, we propose a Vision-Guided Semantic-Group Network (VGSG) for text-based person search to extract well-aligned fine-grained visual and textual features. In the proposed VGSG, we develop a Semantic-Group Textual Learning (SGTL) module and a Vision-guided Knowledge Transfer (VGKT) module to extract textual local features under the guidance of visual local clues. In SGTL, in order to obtain the local textual representation, we group textual features from the channel dimension based on the semantic cues of language expression, which encourages similar semantic patterns to be grouped implicitly without external tools. In VGKT, a vision-guided attention is employed to extract visual-related textual features, which are inherently aligned with visual cues and termed vision-guided textual features. Furthermore, we design a relational knowledge transfer, including a vision-language similarity transfer and a class probability transfer, to adaptively propagate information of the vision-guided textual features to semantic-group textual features. With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction. Experimental results on two challenging benchmarks demonstrate its superiority over state-of-the-art methods.Comment: Accepted to IEEE TI

MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions

This paper strives for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects. Existing referring video object datasets typically focus on salient objects and use language expressions that contain excessive static attributes that could potentially enable the target object to be identified in a single frame. These datasets downplay the importance of motion in video content for language-guided video object segmentation. To investigate the feasibility of using motion expressions to ground and segment objects in videos, we propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments. We benchmarked 5 existing referring video object segmentation (RVOS) methods and conducted a comprehensive comparison on the MeViS dataset. The results show that current RVOS methods cannot effectively address motion expression-guided video segmentation. We further analyze the challenges and propose a baseline approach for the proposed MeViS dataset. The goal of our benchmark is to provide a platform that enables the development of effective language-guided video segmentation algorithms that leverage motion expressions as a primary cue for object segmentation in complex video scenes. The proposed MeViS dataset has been released at https://henghuiding.github.io/MeViS.Comment: ICCV 2023, Project Page: https://henghuiding.github.io/MeViS

MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

Video object segmentation (VOS) aims at segmenting a particular object throughout the entire video clip sequence. The state-of-the-art VOS methods have achieved excellent performance (e.g., 90+% J&F) on existing datasets. However, since the target objects in these existing datasets are usually relatively salient, dominant, and isolated, VOS under complex scenes has rarely been studied. To revisit VOS and make it more applicable in the real world, we collect a new VOS dataset called coMplex video Object SEgmentation (MOSE) to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. The target objects in the videos are commonly occluded by others and disappear in some frames. To analyze the proposed MOSE dataset, we benchmark 18 existing VOS methods under 4 different settings on the proposed MOSE dataset and conduct comprehensive comparisons. The experiments show that current VOS algorithms cannot well perceive objects in complex scenes. For example, under the semi-supervised VOS setting, the highest J&F by existing state-of-the-art VOS methods is only 59.4% on MOSE, much lower than their ~90% J&F performance on DAVIS. The results reveal that although excellent performance has been achieved on existing benchmarks, there are unresolved challenges under complex scenes and more efforts are desired to explore these challenges in the future. The proposed MOSE dataset has been released at https://henghuiding.github.io/MOSE.Comment: MOSE Dataset Repor

Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.Comment: 18 pages, 12 figures, 8 table

Anti-inflammatory effect of dental pulp stem cells

Dental pulp stem cells (DPSCs) have received a lot of attention as a regenerative medicine tool with strong immunomodulatory capabilities. The excessive inflammatory response involves a variety of immune cells, cytokines, and has a considerable impact on tissue regeneration. The use of DPSCs for controlling inflammation for the purpose of treating inflammation-related diseases and autoimmune disorders such as supraspinal nerve inflammation, inflammation of the pulmonary airways, systemic lupus erythematosus, and diabetes mellitus is likely to be safer and more regenerative than traditional medicines. The mechanism of the anti-inflammatory and immunomodulatory effects of DPSCs is relatively complex, and it may be that they themselves or some of the substances they secrete regulate a variety of immune cells through inflammatory immune-related signaling pathways. Most of the current studies are still at the laboratory cellular level and animal model level, and it is believed that through the efforts of more researchers, DPSCs/SHED are expected to be transformed into excellent drugs for the clinical treatment of related diseases

Tunable van Hove singularity without structural instability in Kagome metal CsTi3_3Bi5_5

In Kagome metal CsV$_3$Sb$_5$, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi$_3$Bi$_5$ is a Ti-based Kagome metal to parallel CsV$_3$Sb$_5$. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi$_3$Bi$_5$ samples. Our results reveal that the van Hove singularity (vHS) in CsTi$_3$Bi$_5$ can be tuned in a large energy range without structural instability, different from that in CsV$_3$Sb$_5$. As such, CsTi$_3$Bi$_5$ provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in Kagome metals

Effect of dendrobium mixture in alleviating diabetic cognitive impairment associated with regulating gut microbiota

Dendrobium mixture (DM) is a patent Chinese herbal formulation consisting of Dendrobii Caulis, Astragali Radix, Rehmanniae Radix as the main ingredients. DM has been shown to alleviate diabetic related symptoms attributed to its anti-hyperglycaemic and anti-inflammatory activities. However, the effect on diabetic induced cognitive dysfunction has not been investigated. This study aims to investigate the effect of DM in improving diabetic cognitive impairment and associated mechanisms. Our study confirmed the anti-hyperglycaemic effect of DM and showed its capacity to restore the cognitive and memory function in high fat/high glucose and streptozotocin-induced diabetic rats. The neuroprotective effect was manifested as improved learning and memory behaviours, restored blood-brain barrier tight junction, and enhanced expressions of neuronal survival related biomarkers. DM protected the colon tight junction, and effectively lowered the circulated proinflammatory mediators including tumour necrosis factor-α, interleukin-6 and lipopolysaccharides. In the gut microbiota, DM corrected the increase in the abundance of Firmicutes, the increase in the ratio of Firmicutes/Bacteroidetes, and the decrease in the abundance of Bacteroidetes in diabetic rats. It also reversed the abundance of Lactobacillus, Ruminococcus and Allobaculum genera. Short chain fatty acids, isobutyric acid and ethylmethylacetic acid, were negatively and significantly correlated to Ruminococcus and Allobaculum. Isovaleric acid was positively and significantly correlated with Lactobacillus, which all contributing to the improvement in glucose level, systemic inflammation and cognitive function in diabetic rats. Our results demonstrated the potential of DM as a promising therapeutic agent in treating diabetic cognitive impairment and the underlying mechanism may be associated with regulating gut microbiota