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Multi-Object Tracking (MOT) aims to detect and associate all targets of given classes across frames. Current dominant solutions, e.g. ByteTrack and StrongSORT++, follow the hybrid pipeline, which first accomplish most of the associations in an online manner, and then refine the results using offline tricks such as interpolation and global link. While this paradigm offers flexibility in application, the disjoint design between the two stages results in suboptimal performance. In this paper, we propose the Hierarchical IoU Tracking framework, dubbed HIT, which achieves unified hierarchical tracking by utilizing tracklet intervals as priors. To ensure the conciseness, only IoU is utilized for association, while discarding the heavy appearance models, tricky auxiliary cues, and learning-based association modules. We further identify three inconsistency issues regarding target size, camera movement and hierarchical cues, and design corresponding solutions to guarantee the reliability of associations. Though its simplicity, our method achieves promising performance on four datasets, i.e., MOT17, KITTI, DanceTrack and VisDrone, providing a strong baseline for future tracking method design. Moreover, we experiment on seven trackers and prove that HIT can be seamlessly integrated with other solutions, whether they are motion-based, appearance-based or learning-based. Our codes will be released at https://github.com/dyhBUPT/HIT., Comment: 7 pages, 3 figures

Brain decoding, which aims at reconstructing visual stimuli from brain signals, primarily utilizing functional magnetic resonance imaging (fMRI), has recently made positive progress. However, it is impeded by significant challenges such as the difficulty of acquiring fMRI-image pairs and the variability of individuals, etc. Most methods have to adopt the per-subject-per-model paradigm, greatly limiting their applications. To alleviate this problem, we introduce a new and meaningful task, few-shot brain decoding, while it will face two inherent difficulties: 1) the scarcity of fMRI-image pairs and the noisy signals can easily lead to overfitting; 2) the inadequate guidance complicates the training of a robust encoder. Therefore, a novel framework named MindShot, is proposed to achieve effective few-shot brain decoding by leveraging cross-subject prior knowledge. Firstly, inspired by the hemodynamic response function (HRF), the HRF adapter is applied to eliminate unexplainable cognitive differences between subjects with small trainable parameters. Secondly, a Fourier-based cross-subject supervision method is presented to extract additional high-level and low-level biological guidance information from signals of other subjects. Under the MindShot, new subjects and pretrained individuals only need to view images of the same semantic class, significantly expanding the model's applicability. Experimental results demonstrate MindShot's ability of reconstructing semantically faithful images in few-shot scenarios and outperforms methods based on the per-subject-per-model paradigm. The promising results of the proposed method not only validate the feasibility of few-shot brain decoding but also provide the possibility for the learning of large models under the condition of reducing data dependence.

Referring Expression Segmentation (RES) has attracted rising attention, aiming to identify and segment objects based on natural language expressions. While substantial progress has been made in RES, the emergence of Generalized Referring Expression Segmentation (GRES) introduces new challenges by allowing expressions to describe multiple objects or lack specific object references. Existing RES methods, usually rely on sophisticated encoder-decoder and feature fusion modules, and are difficult to generate class prototypes that match each instance individually when confronted with the complex referent and binary labels of GRES. In this paper, reevaluating the differences between RES and GRES, we propose a novel Model with Adaptive Binding Prototypes (MABP) that adaptively binds queries to object features in the corresponding region. It enables different query vectors to match instances of different categories or different parts of the same instance, significantly expanding the decoder's flexibility, dispersing global pressure across all queries, and easing the demands on the encoder. Experimental results demonstrate that MABP significantly outperforms state-of-the-art methods in all three splits on gRefCOCO dataset. Meanwhile, MABP also surpasses state-of-the-art methods on RefCOCO+ and G-Ref datasets, and achieves very competitive results on RefCOCO. Code is available at https://github.com/buptLwz/MABP, Comment: 11 pages,7 figures

The new trend in multi-object tracking task is to track objects of interest using natural language. However, the scarcity of paired prompt-instance data hinders its progress. To address this challenge, we propose a high-quality yet low-cost data generation method base on Unreal Engine 5 and construct a brand-new benchmark dataset, named Refer-UE-City, which primarily includes scenes from intersection surveillance videos, detailing the appearance and actions of people and vehicles. Specifically, it provides 14 videos with a total of 714 expressions, and is comparable in scale to the Refer-KITTI dataset. Additionally, we propose a multi-level semantic-guided multi-object framework called MLS-Track, where the interaction between the model and text is enhanced layer by layer through the introduction of Semantic Guidance Module (SGM) and Semantic Correlation Branch (SCB). Extensive experiments on Refer-UE-City and Refer-KITTI datasets demonstrate the effectiveness of our proposed framework and it achieves state-of-the-art performance. Code and datatsets will be available., Comment: 17 pages 8 figures

Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless, there exists unseen classes from target domains in practical scenarios. To address this issue, Open Set Domain Generalization (OSDG) has emerged and several methods have been exclusively proposed. However, most existing methods adopt complex architectures with slight improvement compared with DG methods. Recently, vision-language models (VLMs) have been introduced in DG following the fine-tuning paradigm, but consume huge training overhead with large vision models. Therefore, in this paper, we innovate to transfer knowledge from VLMs to lightweight vision models and improve the robustness by introducing Perturbation Distillation (PD) from three perspectives, including Score, Class and Instance (SCI), named SCI-PD. Moreover, previous methods are oriented by the benchmarks with identical and fixed splits, ignoring the divergence between source domains. These methods are revealed to suffer from sharp performance decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a novel metric $H^{2}$-CV, which construct various splits to comprehensively assess the robustness of algorithms. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms on multiple datasets, especially improving the robustness when confronting data scarcity., Comment: Accepted to CVPR2024

Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety mechanisms to prevent system failure. To address this need, we propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset, followed by the extraction of features from the observed circuit signals. Subsequently, we employ clustering algorithms to facilitate anomaly detection. Finally, we propose a time series framework to enhance and expedite anomaly detection performance. Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels, thereby potentially paving the way for the implementation of reliable safety mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings indicate that our approach achieves 100% anomaly detection accuracy and significantly optimizes the associated latency by 5X, underscoring the effectiveness of our devised solution., Comment: 12 pages, 12 figures

Visible-infrared person re-identification (VI-ReID) is challenging due to considerable cross-modality discrepancies. Existing works mainly focus on learning modality-invariant features while suppressing modality-specific ones. However, retrieving visible images only depends on infrared samples is an extreme problem because of the absence of color information. To this end, we present the Refer-VI-ReID settings, which aims to match target visible images from both infrared images and coarse language descriptions (e.g., "a man with red top and black pants") to complement the missing color information. To address this task, we design a Y-Y-shape decomposition structure, dubbed YYDS, to decompose and aggregate texture and color features of targets. Specifically, the text-IoU regularization strategy is firstly presented to facilitate the decomposition training, and a joint relation module is then proposed to infer the aggregation. Furthermore, the cross-modal version of k-reciprocal re-ranking algorithm is investigated, named CMKR, in which three neighbor search strategies and one local query expansion method are explored to alleviate the modality bias problem of the near neighbors. We conduct experiments on SYSU-MM01, RegDB and LLCM datasets with our manually annotated descriptions. Both YYDS and CMKR achieve remarkable improvements over SOTA methods on all three datasets. Codes are available at https://github.com/dyhBUPT/YYDS., Comment: 14 pages, 6 figures

The enhanced Coulomb interaction in two-dimensional (2D) semiconductors leads to the tightly bound electron-hole pairs known as excitons. The large binding energy of excitons enables the formation of Rydberg excitons with high principal quantum numbers (n), analogous to Rydberg atoms. Rydberg excitons possess strong interactions among themselves, as well as sensitive responses to external stimuli. Here, we probe Rydberg exciton resonances through photocurrent spectroscopy in a monolayer WSe2 p-n junction formed by a split-gate geometry. We show that an external in-plane electric field not only induces a large Stark shift of Rydberg excitons up to quantum principal number n=3 but also mixes different orbitals and brightens otherwise dark states such as 3p and 3d. Our study provides an exciting platform for engineering Rydberg excitons for new quantum states and quantum sensing.

Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions. Previous works realize it by simply integrating an extra textual module into the multi-object tracker. However, they typically need to retrain the entire framework and have difficulties in optimization. In this work, we propose an insertable Knowledge Unification Network, termed iKUN, to enable communication with off-the-shelf trackers in a plug-and-play manner. Concretely, a knowledge unification module (KUM) is designed to adaptively extract visual features based on textual guidance. Meanwhile, to improve the localization accuracy, we present a neural version of Kalman filter (NKF) to dynamically adjust process noise and observation noise based on the current motion status. Moreover, to address the problem of open-set long-tail distribution of textual descriptions, a test-time similarity calibration method is proposed to refine the confidence score with pseudo frequency. Extensive experiments on Refer-KITTI dataset verify the effectiveness of our framework. Finally, to speed up the development of RMOT, we also contribute a more challenging dataset, Refer-Dance, by extending public DanceTrack dataset with motion and dressing descriptions. The codes and dataset are available at https://github.com/dyhBUPT/iKUN., Comment: CVPR 2024 camera-ready

Searching for specific person has great social benefits and security value, and it often involves a combination of visual and textual information. Conventional person retrieval methods, whether image-based or text-based, usually fall short in effectively harnessing both types of information, leading to the loss of accuracy. In this paper, a whole new task called Composed Person Retrieval (CPR) is proposed to jointly utilize both image and text information for target person retrieval. However, the supervised CPR requires very costly manual annotation dataset, while there are currently no available resources. To mitigate this issue, we firstly introduce the Zero-shot Composed Person Retrieval (ZS-CPR), which leverages existing domain-related data to resolve the CPR problem without expensive annotations. Secondly, to learn ZS-CPR model, we propose a two-stage learning framework, Word4Per, where a lightweight Textual Inversion Network (TINet) and a text-based person retrieval model based on fine-tuned Contrastive Language-Image Pre-training (CLIP) network are learned without utilizing any CPR data. Thirdly, a finely annotated Image-Text Composed Person Retrieval (ITCPR) dataset is built as the benchmark to assess the performance of the proposed Word4Per framework. Extensive experiments under both Rank-1 and mAP demonstrate the effectiveness of Word4Per for the ZS-CPR task, surpassing the comparative methods by over 10\%. The code and ITCPR dataset will be publicly available at https://github.com/Delong-liu-bupt/Word4Per.

Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists. Although automatic diagnosis SRCs based on deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy, the existing works have not been systematically overviewed, which hindered the evaluation of the gap between algorithms and clinical applications. In this paper, we provide a survey on SRC analysis driven by deep learning from 2008 to August 2023. Specifically, the biological characteristics of SRCs and the challenges of automatic identification are systemically summarized. Then, the representative algorithms are analyzed and compared via dividing them into classification, detection, and segmentation. Finally, for comprehensive consideration to the performance of existing methods and the requirements for clinical assistance, we discuss the open issues and future trends of SRC analysis. The retrospect research will help researchers in the related fields, particularly for who without medical science background not only to clearly find the outline of SRC analysis, but also gain the prospect of intelligent diagnosis, resulting in accelerating the practice and application of intelligent algorithms.

Moir\'e superlattices of semiconducting transition metal dichalcogenides (TMDCs) enable unprecedented spatial control of electron wavefunctions in an artificial lattice with periodicities more than ten times larger than that of atomic crystals, leading to emerging quantum states with fascinating electronic and optical properties. The breaking of translational symmetry further introduces a new degree of freedom inside each moir\'e unit cell: high symmetry points of energy minima called moir\'e sites, behaving as spatially separated quantum dots. The superposition of a quasiparticle wavefunction between different moir\'e sites will enable a new platform for quantum information processing but is hindered by the suppressed electron tunneling between moir\'e sites. Here we demonstrate the superposition between two moir\'e sites by constructing an angle-aligned trilayer WSe2/monolayer WS2 moir\'e heterojunction. The two moir\'e sites with energy minimum allow the formation of two different interlayer excitons, with the hole residing in either moir\'e site of the first WSe2 layer interfacing the WS2 layer and the electron in the third WSe2 layer. An external electric field can drive the hybridization of either of the interlayer excitons with the intralayer excitons in the third WSe2 layer, realizing the continuous tuning of interlayer exciton hopping between two moir\'e sites. Therefore, a superposition of the two interlayer excitons localized at different moir\'e sites can be realized, which can be resolved in the electric-field-dependent optical reflectance spectra, distinctly different from that of the natural trilayer WSe2 in which the moir\'e modulation is absent. Our study illustrates a strategy of harnessing the new moir\'e site degree of freedom for quantum information science, a new direction of twistronics.

Strongly enhanced electron-electron interaction in semiconducting moir\'e superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moir\'e superlattice, with electrons and holes separated in different layers, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moir\'e superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moir\'e cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moir\'e superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both.

Moir\'e superlattices of transitional metal dichalcogenides exhibit strong electron-electron interaction that has led to experimental observations of Mott insulators and generalized Wigner crystals. In this letter, we report direct measurements of the thermodynamic gaps of these correlated insulating states in a dual-gate WS2/WSe2 moir\'e bilayer. We employ the microwave impedance microscopy to probe the electronic features in both the graphene top gate and the moir\'e bilayer, from which we extract the doping dependence of the chemical potential of the moir\'e bilayer and the energy gaps for various correlated insulating states utilizing the Landau quantization of graphene. These gaps are relatively insensitive to the application of an external electric field to the WS2/WSe2 moir\'e bilayer.

Transition metal dichalcogenide (TMDC) moir\'e superlattices, owing to the moir\'e flatbands and strong correlation, can host periodic electron crystals and fascinating correlated physics. The TMDC heterojunctions in the type-II alignment also enable long-lived interlayer excitons that are promising for correlated bosonic states, while the interaction is dictated by the asymmetry of the heterojunction. Here we demonstrate a new excitonic state, quadrupolar exciton, in a symmetric WSe2-WS2-WSe2 trilayer moir\'e superlattice. The quadrupolar excitons exhibit a quadratic dependence on the electric field, distinctively different from the linear Stark shift of the dipolar excitons in heterobilayers. This quadrupolar exciton stems from the hybridization of WSe2 valence moir\'e flatbands. The same mechanism also gives rise to an interlayer Mott insulator state, in which the two WSe2 layers share one hole laterally confined in one moir\'e unit cell. In contrast, the hole occupation probability in each layer can be continuously tuned via an out-of-plane electric field, reaching 100% in the top or bottom WSe2 under a large electric field, accompanying the transition from quadrupolar excitons to dipolar excitons. Our work demonstrates a trilayer moir\'e system as a new exciting playground for realizing novel correlated states and engineering quantum phase transitions.

Prototype-based classification is a classical method in machine learning, and recently it has achieved remarkable success in semi-supervised semantic segmentation. However, the current approach isolates the prototype initialization process from the main training framework, which appears to be unnecessary. Furthermore, while the direct use of K-Means algorithm for prototype generation has considered rich intra-class variance, it may not be the optimal solution for the classification task. To tackle these problems, we propose a novel boundary-refined prototype generation (BRPG) method, which is incorporated into the whole training framework. Specifically, our approach samples and clusters high- and low-confidence features separately based on a confidence threshold, aiming to generate prototypes closer to the class boundaries. Moreover, an adaptive prototype optimization strategy is introduced to make prototype augmentation for categories with scattered feature distributions. Extensive experiments on the PASCAL VOC 2012 and Cityscapes datasets demonstrate the superiority and scalability of the proposed method, outperforming the current state-of-the-art approaches. The code is available at xxxxxxxxxxxxxx., Comment: 53 pages, 7 figures

Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the relationship between module parameters of Re-ID framework and feature distributions, which may lead to feature misalignment and hinder the model performance. To address this problem, we propose a dynamic clustering and cluster contrastive learning (DCCC) method. Specifically, we first design a dynamic clustering parameters scheduler (DCPS) which adjust the hyper-parameter of clustering to fit the variation of intra- and inter-class distances. Then, a dynamic cluster contrastive learning (DyCL) method is designed to match the cluster representation vectors' weights with the local feature association. Finally, a label smoothing soft contrastive loss ($L_{ss}$) is built to keep the balance between cluster contrastive learning and self-supervised learning with low computational consumption and high computational efficiency. Experiments on several widely used public datasets validate the effectiveness of our proposed DCCC which outperforms previous state-of-the-art methods by achieving the best performance.

Multiple Object Tracking (MOT) has rapidly progressed in recent years. Existing works tend to design a single tracking algorithm to perform both detection and association. Though ensemble learning has been exploited in many tasks, i.e, classification and object detection, it hasn't been studied in the MOT task, which is mainly caused by its complexity and evaluation metrics. In this paper, we propose a simple but effective ensemble method for MOT, called EnsembleMOT, which merges multiple tracking results from various trackers with spatio-temporal constraints. Meanwhile, several post-processing procedures are applied to filter out abnormal results. Our method is model-independent and doesn't need the learning procedure. What's more, it can easily work in conjunction with other algorithms, e.g., tracklets interpolation. Experiments on the MOT17 dataset demonstrate the effectiveness of the proposed method. Codes are available at https://github.com/dyhBUPT/EnsembleMOT., Comment: 5 pages, 1 figure

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2. The codes are available at https://github.com/dyhBUPT/OMG., Comment: CVPR 2022 Workshop

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly, remarkable progresses have been achieved. However, the existing methods tend to use various basic models (e.g, detector and embedding model), and different training or inference tricks, etc. As a result, the construction of a good baseline for a fair comparison is essential. In this paper, a classic tracker, i.e., DeepSORT, is first revisited, and then is significantly improved from multiple perspectives such as object detection, feature embedding, and trajectory association. The proposed tracker, named StrongSORT, contributes a strong and fair baseline for the MOT community. Moreover, two lightweight and plug-and-play algorithms are proposed to address two inherent "missing" problems of MOT: missing association and missing detection. Specifically, unlike most methods, which associate short tracklets into complete trajectories at high computation complexity, we propose an appearance-free link model (AFLink) to perform global association without appearance information, and achieve a good balance between speed and accuracy. Furthermore, we propose a Gaussian-smoothed interpolation (GSI) based on Gaussian process regression to relieve the missing detection. AFLink and GSI can be easily plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). Finally, by fusing StrongSORT with AFLink and GSI, the final tracker (StrongSORT++) achieves state-of-the-art results on multiple public benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking., Comment: Accepted by IEEE Transactions on Multimedia 2023

In the archetypal monolayer semiconductor WSe$_2$, the distinct ordering of spin-polarized valleys (low-energy pockets) in the conduction band allows for studies of not only simple neutral excitons and charged excitons (i.e., trions), but also more complex many-body states that are predicted at higher electron densities. We discuss magneto-optical measurements of electron-rich WSe$_2$ monolayers, and interpret the spectral lines that emerge at high electron doping as optical transitions of 6-body exciton states ("hexcitons") and 8-body exciton states ("oxcitons"). These many-body states emerge when a photoexcited electron-hole pair interacts simultaneously with multiple Fermi seas, each having distinguishable spin and valley quantum numbers. In addition, we identify the energies of primary and satellite optical transitions of hexcitons in the photoluminescence spectrum.