Siamese network object tracking is a widely employed tracking method due to its simplicity and effectiveness. It first employs a two-stream network to independently extract template and search region features. Subsequently, these features are then combined through feature association to yield object information within the visual scene. However, the conventional approach faces limitations when it leverages the template features as a convolution kernel to convolve the search image features, which restricts the ability to capture complex and nonlinear feature transformations of objects, thereby restricting its discriminative capabilities. To overcome this challenge, we propose replacing traditional convolutional correlation with Slot Attention for feature association. This novel approach enables the effective extraction of nonlinear features within the scene, while augmenting the discriminative capacity. Furthermore, to increase the inference efficiency and reduce the parameter occupation, we suggest deploying a single Slot Attention module for multiple associations. Our tracking algorithm, SiamSlot, was evaluated on diverse benchmarks, including VOT2019, GOT-10k, UAV123, and Nfs. The experiments show a remarkable improvement in performance relative to previous methods under the same network size.

Linear array detector-based infrared push-broom imaging systems are widely employed in remote sensing and security surveillance due to their high spatial resolution, wide swath coverage, and low cost. However, the massive data volume generated during continuous scanning presents substantial storage and transmission challenges. To mitigate this issue, we propose a lossless compression algorithm based on pixel-adaptive prediction and hierarchical decomposition of residuals. The algorithm first performs pixel-wise adaptive noise compensation according to local image characteristics and achieves efficient prediction by exploiting the strong inter-pixel correlation along the scanning direction. Subsequently, hierarchical decomposition is applied to high-energy residual blocks to further eliminate spatial redundancy. Finally, the Golomb–Rice coding parameters are adaptively adjusted based on the neighborhood residual energy, optimizing the overall code length distribution. The experimental results demonstrate that our method significantly outperforms most state-of-the-art approaches in terms of both the compression ratio (CR) and bits per pixel (BPP). Moreover, while maintaining a CR comparable to H.265-Intra, our method achieves a 21-fold reduction in time complexity, confirming its superiority for large-format image compression.