Beyond Appearance: Multi-Frame Spatio-Temporal Context Memory Networks for Efficient and Robust Video Object Segmentation.

Current video object segmentation approaches primarily rely on frame-wise appearance information to perform matching. Despite significant progress, reliable matching becomes challenging due to rapid changes of the object's appearance over time. Moreover, previous matching mechanisms suffer from redundant computation and noise interference as the number of accumulated frames increases.

Adaptive Sparse Memory Networks for Efficient and Robust Video Object Segmentation.

Recently, memory-based networks have achieved promising performance for video object segmentation (VOS). However, existing methods still suffer from unsatisfactory segmentation accuracy and inferior efficiency. The reasons are mainly twofold: 1) during memory construction, the inflexible memory storage mechanism results in a weak discriminative ability for similar appearances in complex scenarios, leading to video-level temporal redundancy, and 2) during memory reading, matching robustness and memory retrieval accuracy decrease as the number of video frames increases.

Efficient and Robust Video Object Segmentation Through Isogenous Memory Sampling and Frame Relation Mining.

Recently, memory-based methods have achieved remarkable progress in video object segmentation. However, the segmentation performance is still limited by error accumulation and redundant memory, primarily because of 1) the semantic gap caused by similarity matching and memory reading via heterogeneous key-value encoding; 2) the continuously growing and inaccurate memory through directly storing unreliable predictions of all previous frames. To address these issues, we propose an efficient, effective, and robust segmentation method based on Isogenous Memory Sampling and Frame-Relation mining (IMSFR).

Laplacian-Uniform Mixture-Driven Iterative Robust Coding With Applications to Face Recognition Against Dense Errors.

Outliers due to occlusion, pixel corruption, and so on pose serious challenges to face recognition despite the recent progress brought by sparse representation. In this article, we show that robust statistics implemented by the state-of-the-art methods are insufficient for robustness against dense gross errors. By modeling the distribution of coding residuals with a Laplacian-uniform mixture, we obtain a sparse representation that is significantly more robust than the previous methods.

Link Between and Comparison and Combination of Zhang Neural Network and Quasi-Newton BFGS Method for Time-Varying Quadratic Minimization.

Since 2001, a novel type of recurrent neural network called Zhang neural network (ZNN) has been proposed, investigated, and exploited for solving online time-varying problems in a variety of scientific and engineering fields. In this paper, three discrete-time ZNN models are first proposed to solve the problem of time-varying quadratic minimization (TVQM). Such discrete-time ZNN models exploit methodologically the time derivatives of time-varying coefficients and the inverse of the time-varying coefficient matrix.

Fast-learning adaptive-subspace self-organizing map: an application to saliency-based invariant image feature construction.

The adaptive-subspace self-organizing map (ASSOM) is useful for invariant feature generation and visualization. However, the learning procedure of the ASSOM is slow. In this paper, two fast implementations of the ASSOM are proposed to boost ASSOM learning based on insightful discussions of the basis rotation operator of ASSOM.