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Similarity-based approaches have made significant progress in visual object tracking (VOT). Although these methods work well in simple scenes, they ignore the continuous spatio-temporal connection of the object in the video sequence. For this reason, tracking by spatial matching solely can lead to tracking failures because of distractors and occlusion. In this paper, we propose a spatio-temporal joint-modeling tracker named STTrack which implicitly builds continuous connections between the temporal and spatial aspects of the sequence. Specifically, we first design a time-sequence iteration strategy (TSIS) to concentrate on the temporal connection of the object in the video sequence. Then, we propose a novel spatial temporal interaction Transformer network (STIN) to capture the spatio-temporal correlation of the object between frames. The proposed STIN module is robust in object occlusion because it explores the dynamic state change dependencies of the object. Finally, we introduce a spatio-temporal query to suppress distractors by iteratively propagating the target prior. Extensive experiments on six tracking benchmark datasets demonstrate that the proposed STTrack achieves excellent performance while operating in real-time. The code is publicly available at https://github.com/nubsym/STTrack. [ABSTRACT FROM AUTHOR]

Highlights: [•] We improve the microwave absorbing properties of FeSiAl composite via moderate surface oxidation. [•] The FeSiAl composite possesses inferior permittivity and superior permeability. [•] The minimum RL and peak frequency are −39.67dB and 1.40GHz at the thickness of 4mm. [•] The FeSiAl composite can be applied in the low frequency (L and S band). [ABSTRACT FROM AUTHOR]

Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. [ABSTRACT FROM AUTHOR]

• It is necessary to reduce the training costs of current tracking frameworks. • Our model can realize the attention of equivalent linear complexity. • Our linearization scheme can be used for both self-attention and cross-attention. • Our model achieves stable tracking performance with high robustness and fast speed. Mainstream tracking approaches have achieved remarkable performance by adopting transformer structures. However, transformer structures' inherent design of dot-product with softmax normalization incurs quadratic computation complexity regarding sequence length. This issue is further complicated when vision tasks employ softmax attention, as sequence length scales with the square of images' sizes. Even though sparse attention and low-rank decomposition can alleviate over-inflated computation, it is still laborious to balance trackers' accuracy, computation cost, and inference speed. To tackle the above problems, we propose an Information-Diffused Graph tracking pipeline with linear complexity (IDGtrack). As the feature constraint relationship in the physical world is an important cue for vision tasks, graph modules are constructed with information-diffused adjacency matrices to substitute softmax attention, which is not only efficient for linear computations but also maintains the non-negativity and global distribution of the attention matrix. Distinct from traditional linear attention methods exclusive to self-attention, a self-integrated and cross-context graph module with linear complexity is explored where a complete bipartite graph is established between the target and search region, facilitating a comprehensive perception of local and background information. Extensive experiments are conducted on public tracking benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) performance with 111 FPS on GPU RTX3090. [ABSTRACT FROM AUTHOR]