Symmetric, high-dimensional and sparse (SHiDS) networks usually contain rich knowledge regarding various patterns. To adequately extract useful information from SHiDS networks, a novel biased triple factorization-based (TF) symmetric and non-negative latent factor (SNLF) model is put forward by utilizing the transfer learning (TL) method, namely biased TL-incorporated TF-SNLF (BT$^{2}$-SNLF) model. The proposed BT$^{2}$-SNLF model mainly includes the following four ideas: 1) the implicit knowledge of the auxiliary matrix in the ternary rating domain is transferred to the target matrix in the numerical rating domain, facilitating the feature extraction; 2) two linear bias vectors are considered into the objective function to discover the knowledge describing the individual entity-oriented effect; 3) an improved momentum-incorporated additive gradient descent algorithm is developed to speed up the model convergence as well as guarantee the non-negativity of target SHiDS networks; and 4) a rigorous proof is provided to show that, under the assumption that the objective function is$L$-smooth and$\mu$-convex, when$t\geq t_{0}$, the algorithm begins to descend and it can find an$\epsilon$-solution within$O(ln((1+\frac{\mu L}{L(1+\mu )+8\mu })/\epsilon ))$. Experimental results on six datasets from real applications demonstrate the effectiveness of our proposed T$^{2}$-SNLF and BT$^{2}$-SNLF models.

Relying on a large amount of data, recent object trackers achieve superior performance. However Siamese-like trackers expose considerable shortcomings in the case of brief occlusion. To address these shortages, the paper proposes a novel pre-frame auxiliary and fusion tracking framework. Within this framework, a retained variable is first introduced to avoid some additional twin branches while retaining the previously obtained deep features of the search frames. Based on such a variable, a pre-frame auxiliary module is constructed to establish the relationship between encoding features and the retained pre-frame information and a decoding fusion module is designed to fuse the generated similarity relationship. Moreover, the Efficient IoU (EIoU) loss is employed to increase the precision of predicted bounding boxes by adding three penalty terms for the differences in the center point, length, and width of the two bounding boxes. Finally, the superiority over state-of-the-art methods is verified by numerous tests on visual tracking benchmarks.