Video moment retrieval (VMR) aims to localize a video segment in an untrimmed video that is semantically relevant to a language query. The challenge of this task lies in effectively aligning the intricate and information-dense video modality with the succinctly summarized textual modality, and further localizing the starting and ending timestamps of the target moments. Previous works have attempted to achieve multi-granularity alignment of video and query in a coarse-to-fine manner, yet these efforts still fall short in addressing the inherent disparities in representation and information density between videos and queries, leading to modal misalignments. In this paper, we propose a progressive video moment retrieval framework, initially retrieving the most relevant and irrelevant video clips to the query as semantic guidance, thereby bridging the semantic gap between video modality and language modality. Futhermore, we introduce a pseudo clips guided aggregation module to aggregate densely relevant moment clips closer together and propose a discriminative boundary-enhanced decoder with the guidance of pseudo clips to push the semantically confusing proposals away. Extensive experiments on the Charades-STA, ActivityNet Captions and TACoS datasets demonstrate that our method outperforms existing methods.

In this paper, a novel patch-wise saliency detection algorithm is proposed based on Principal Component Analysis (PCA). As a powerful statistical procedure in data analysis, PCA are fully exploited to convert color space and produce compact patch representation. Specifically, images are first converted to linearly uncorrelated channels and divided into non-overlapped patches. Then the patches are represented by the coefficients of principal components using PCA analysis. Based on the compact representation of patches, two types of distinctiveness are introduced: center-surround contrast and global rarity. Experimental results demonstrate that the PCA-based color space conversion and patch representation can improve the accuracy of human fixations prediction, and the proposed algorithm outperforms the mainstream algorithms on predicting human fixations.

In this paper, a novel spatial error concealment (EC) algorithm is proposed. Under the sequential recovery framework, pixels in missing blocks are successively reconstructed based on adaptive linear predictor. The predictor automatically tunes its order and support shape according to local contexts. The predictor order and support shape are determined using Bayesian information criterion, which is able to strike a balance between the bias and variance of the prediction errors. The flexibility of the order-adaptive predictor is able to recover more important features or structures. A novel scan order based on the uncertainty of each pixel is also proposed to alleviate error propagation problem. Compared with the state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations.

In this paper, we propose a novel spatial error concealment algorithm based on adaptive linear predictor. The predictor automatically tunes its order and support shape according to local contexts. The optimal order is determined using Bayesian Index Criterion (BIC). The order-adaptive predictor is able to recover more important features or structures. Simulation results show that compared to the state-of-the-art error concealment algorithms, the proposed method gives better reconstruction performance in terms of objective and subjective evaluations.

This paper presents an edge-directed, noniterative image interpolation algorithm. In the proposed algorithm, the gradient directions are explicitly estimated with a statistical-based approach. The local dominant gradient directions are obtained by using principal components analysis (PCA) on the four nearest gradients. The angles of the whole gradient plane are divided into four parts, and each gradient direction falls into one part. Then we implement the interpolation with one-dimention (1-D) cubic convolution interpolation perpendicular to the gradient direction. Compared to the state of-the-art interpolation methods, simulation results show that the proposed PCA-based edge-directed interpolation method preserves edges well while maintaining a high PSNR value.