Fuzzy$k$-Means with Entropy Regularization method (ERFKM) is an extension to Fuzzy$k$-Means (FKM) by introducing a maximum entropy term to FKM, whose purpose is trading off fuzziness and compactness. However, ERFKM often converges to a poor local minimum, which affects its performance. In this paper, we propose an effective optimization method to solve this problem, called IRW-ERFKM. First a new equivalent problem for ERFKM is proposed; then we solve it through Iteratively Re-Weighted (IRW) method. Since IRW-ERFKM optimizes the problem with$k\times 1$instead of$d\times k$intermediate variables, the space complexity of IRW-ERFKM is greatly reduced. Extensive experiments on clustering performance and objective function value show IRW-ERFKM can get a better local minimum than ERFKM with fewer iterations. Through time complexity analysis, it verifies IRW-ERFKM and ERFKM have the same linear time complexity. Moreover, IRW-ERFKM has advantages on evaluation metrics compared with other methods. What's more, there are two interesting findings. One is when we use IRW method to solve the equivalent problem of ERFKM with one factor$\mathbf{U}$, it is equivalent to ERFKM. The other is when the inner loop of IRW-ERFKM is executed only once, IRW-ERFKM and ERFKM are equivalent in this case.

Nighttime image dehazing is critical for many computer applications. Directly transferring daytime dehazing models to nighttime scenes often introduces haze residual, detail loss and color distortion for the uneven distribution by artificial lights. Therefore, we propose a nighttime dehazing method by defining the Dual-domain Feature Learning Module (DFLM) and the Feature Optimization Module (FOM). Firstly, we construct the DFLM in both frequency and spatial domains to accurately predict the image degradation caused by haze and remove most haze in nighttime hazy images. Secondly, to address the challenges of uneven illumination distribution and color interference of light sources in nighttime, we construct the FOM based on the proposed Cross Dimension Interaction Attention (CDIA), which captures the feature dependencies by crossing different dimensions including the channel-channel, height-channel and width-channel. By precisely representing illumination and color features, the FOM alleviates color distortion in nighttime dehazing. Extensive experiments on several synthetic and real-world datasets demonstrate that our method outperforms most state-of-the-art methods. Code will be available.

Matching-based methods have becamed popular in semi-supervised video object segmentation (VOS), by maintaining a memory bank to predict object masks. However, these methods encounter challenges for fast motions and appearance changes, resulting in blurred predictions and missing boundaries. Then we introduce an innovative network that exploits the self-feature of the query frame to improve the masks prediction. We propose a semantic-aware branch (SAB) for precise semantic guidance during readout decoding and an enhanced feature memory matching module with a self-support matching (SSM) mechanism. Ablations demonstrate the strong collaboration between the semantic-aware branch and the self-support matching mechanism. Our approach achieves a favourable performance on popular datasets, demonstrating a acceptable accuracy and speed performance of 86.3 J&F and 26 FPS on DAVIS 2017 validation. Code will be available.

Recently, Transformer based visual object tracking methods have achieved impressive advancements and significantly improved tracking performance. Transformer includes two modules of self-attention and cross-attention for those methods. However, it brings up two problems: first, the self-attention only considers the relative relation between elements when establishing global association, which can not highlight the essential areas of the tracked target. Second, the cross-attention only relies on feature similarity to locate the target, where the interference of similar objects is challenging. In this paper, we propose a new transformer tracking method of GTTrack by defining Gaussian Attention (GA) and Adaptive Focusing Module (AFM). The GA leads into Gaussian prior to generate a semantic template with robust object features, in which Gaussian prior pays more attention to the central region of the tracked target. The AFM calculates the similarity between current frame and the template by combining the appearance features and position features. The position features are defined with an adaptive Gaussian prior according to the target area in the previous frame. The introduction of position features enhances the contrast between the tracked target and the similar objects. Extensive experiments also demonstrate that the GTTrack outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.