Video object segmentation (VOS) plays an important role in video analysis and understanding, which in turn facilitates a number of diverse applications, including video editing, video rendering, and augmented reality / virtual reality. However, existing deep learning-based approaches rely heavily on a large number of pixel-wise annotated video frames to achieve promising results, which is notoriously laborious and costly. To address this, in this paper, we formulate unsupervised video object detection by exploring simulated dense labels and explicit motion clues. Specifically, we first propose an effective video label generator network based on the sparsely annotated frames and the flow motion between them. It can largely alleviate our dependence and limitation on the sparse labels. Furthermore, we propose a transformer-based architecture to model the appearance and motion clues simultaneously with the cross-attention module, in order to maximally overcome non-linear motion with potential occlusions. Extensive experiments show that the proposed method outperforms recent VOS methods on four popular benchmarks (i.e., DAVIS-16, FBMS, Youtube-VOS and SegTrack-v2). Moreover, the proposed method can be further applied to a wide range of wild scenes such as wild forests and animals. Because of its effectiveness and generalization, we believe that our method could serve as a useful basis for alleviating the dependence on dense annotation in video data.

Abstract Salp Swarm Algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm Algorithm, we proposed a Salp Swarm Algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm Algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm Algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm Algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame Optimization, Grasshopper Optimization, and Ant Lion Optimization.

Numerous tracking approaches attempt to improve target representation through target-aware or distractor-aware. However, the unbalanced considerations of target or distractor information make it diffcult for these methods to benefit from the two aspects at the same time. In this paper, we propose a target-distractor aware model with discriminative enhancement learning loss to learn target representation, which can better distinguish the target in complex scenes. Firstly, to enlarge the gap between the target and distractor, we design a discriminative enhancement learning loss. By highlighting the hard negatives that are similar to the target and shrinking the easy negatives that are pure background, the features sensitive to the target or distractor representation can be more conveniently mined. On this basis, we further propose a target-distractor aware model. Unlike existing methods of preference target or distractor, we construct the target-specific feature space by activating the target-sensitive and the distractor-silence feature. Therefore, the appearance model can not only represent the target well but also suppress the background distractor. Finally, the target-distractor aware target representation model is integrated with a Siamese matching network for visual tracking for achieving robust and realtime visual tracking. Extensive experiments are performed on eight tracking benchmarks show that the proposed algorithm achieves favorable performance.

Ant lion optimiser (ALO) is a new nature‐inspired swarm intelligence optimisation algorithm that mimics the hunting mechanism of antlions in nature. ALO has been proved to have the merits of high exploitation and convergence speed benefiting from adaptive boundary shrinking mechanism and elitism. In this work, visual tracking is expressed as searching for object in whole search space by interaction between antlions and ants. A novel ALO‐based visual tracking framework is proposed and the adaptation and sensitivity of the parameters in ALO are discussed to improve tracking performance. In addition, considering that ALO tracker needs a lot of iteration consumption, kernel correlation filter with deep feature is integrated into the ALO tracking framework (ALOKCF) to improve track efficiency. Extensive experimental results prove that the ALO tracker is very competitive compared to other trackers, especially for abrupt motion tracking. At the same time, two visual tracking benchmarks are used to verify ALOKCF tracker achieves state‐of‐the‐art performance.