Recent advances in multi-object tracking (MOT) have demonstrated significant success in short-term association within the separated tracking-by-detection online paradigm. However, long-term tracking remains challenging. While graph-based approaches address this by modeling trajectories as global graphs, these methods are unsuitable for real-time applications due to their non-online nature. In this article, we review the concept of trajectory graphs and propose a novel perspective by representing them as directed acyclic graphs. This representation can be described using frame-ordered object sequences and binary adjacency matrices. We observe that this structure naturally aligns with Transformer attention mechanisms, enabling us to model the association problem using a classic Transformer architecture. Based on this insight, we introduce a concise pure transformer (PuTR) to validate the effectiveness of Transformer in unifying short- and long-term tracking for separated online MOT. Extensive experiments on four diverse datasets (SportsMOT, DanceTrack, MOT17, and MOT20) demonstrate that PuTR effectively establishes a solid baseline compared to existing foundational online methods while exhibiting superior domain adaptation capabilities. Furthermore, the separated nature enables efficient training and inference, making it suitable for practical applications. Implementation code and trained models are available at https://github.com/chongweiliu/PuTR .

Detecting time-series anomalies is extremely intricate due to the rarity of anomalies and imbalanced sample categories, which often result in costly and challenging anomaly labeling. Most of the existing approaches largely depend on assumptions of normality, overlooking labeled abnormal samples. While anomaly assumptions based methods can incorporate prior knowledge of anomalies for data augmentation in training classifiers, the adopted random or coarse-grained augmentation approaches solely focus on pointwise anomalies and lack cutting-edge domain knowledge, making them less likely to achieve better performance. This paper introduces CutAddPaste, a novel anomaly assumption-based approach for detecting time-series anomalies. It primarily employs a data augmentation strategy to generate pseudo anomalies, by exploiting prior knowledge of anomalies as much as possible. At the core of CutAddPaste is cutting patches from random positions in temporal subsequence samples, adding linear trend terms, and pasting them into other samples, so that it can well approximate a variety of anomalies, including point and pattern anomalies. Experiments on standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.

In the field of Multi-Object Tracking (MOT), the incorporation of appearance cues into tracking-by-detection heuristic trackers using re-identification (ReID) features has posed limitations on its advancement. The existing ReID paradigm involves the extraction of coarse-grained object-level feature vectors from cropped objects at a fixed input size using a ReID model, and similarity computation through a simple normalized inner product. However, MOT requires fine-grained features from different object regions and more accurate similarity measurements to identify individuals, especially in the presence of occlusion. To address these limitations, we propose a novel feature paradigm. In this paradigm, we extract the feature map from the entire frame image to preserve object sizes and represent objects using a set of fine-grained features from different object regions. These features are sampled from adaptive patches within the object bounding box on the feature map to effectively capture local appearance cues. We introduce Mutual Ratio Similarity (MRS) to accurately measure the similarity of the most discriminative region between two objects based on the sampled patches, which proves effective in handling occlusion. Moreover, we propose absolute Intersection over Union (AIoU) to consider object sizes in feature cost computation. We integrate our paradigm with advanced motion techniques to develop a heuristic Motion-Feature joint multi-object tracker, MoFe. Within it, we reformulate the track state transition of tracklets to better model their life cycle, and firstly introduce a runtime recorder after MoFe to refine trajectories. Extensive experiments on five benchmarks, i.e., GMOT-40, BDD100k, DanceTrack, MOT17, and MOT20, demonstrate that MoFe achieves state-of-the-art performance in robustness and generalizability without any fine-tuning, and even surpasses the performance of fine-tuned ReID features.

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self- supervised deep learning task. Single-normality-assumption- based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the original and reconstructed representations as the positive pair of negative-sample-free CL, namely “sequence contrast”. Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the “hypersphere collapse” is prevented by variance terms. In addition, extensive experiments on two real- world time-series datasets show the superior performance of the proposed method achieves state-of-the-art. *The full version of the paper can be accessed at https://arxiv.org/abs/2207.01472

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively. Specifically, Poisson GAN combines Poisson blending into its generator and employs a new loss called Dual Restriction loss (DR loss), which supervises both implicit space features and image-level features during training to generate more realistic images. By utilizing Poisson GAN, objects of minority class like seacucumber or scallop could be added into an image naturally and annotated automatically, which could increase the loss of minority classes during training detectors to eliminate the class-imbalance problem; AquaNet is a high-efficiency detector to address the problem of detecting mass small objects from cloudy underwater pictures. Within it, we design two efficient components: a depth-wise-convolution-based Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale Blursampling (MBP) module to reduce the parameters of the network to 1.3 million. Both two components could provide multi-scale features of small objects under a short backbone configuration without any loss of accuracy. In addition, we construct a large-scale augmented dataset (AUDD) and a pre-training dataset via Poisson GAN from UDD. Extensive experiments show the effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training dataset.