Existing S equential R ecommendation (SR) methods have conventionally viewed historical interactions as one-dimensional sequences, often overlooking the fact that user behaviors can be multi-faceted and uncertain. Such a straightforward perspective fails to account for varied behavior patterns embedded in the historical sequences. Moving beyond adhering to singular historical sequences, we treat augmented sequences as meaningful behavior patterns and jointly optimize all sequences (augmented and original sequences) to capture diverse patterns, intricate dependencies, and uncertainties. To acknowledge and distinguish new patterns derived from the original sequence, we develop a sequential order-enhanced method to calculate the edge weight, highlighting the unique dependency relationships inherent in each individual sequence. To prevent recommendations from becoming monotonous due to similarities in augmented sequences, we apply a repulsive mechanism to sequences with similar topics/categories, ensuring a broader spectrum of suggestions. Considering the potent expressive capability of the probabilistic model, Structured Determinantal Point Processes (SDPP), in representing structures, we perceive original and augmented sequences as such structures, leading to our generic learning framework S tructured S equential Rec ommendation (SSRec), which is theoretically proved to be a structured ranking optimization criterion . Comprehensive experiments on real-world datasets demonstrate SSRec’s distinct advantages over state-of-the-art models in terms of both diversity and accuracy.

Color propagation aims to extend local color edits to similar regions across the input image. Conventional approaches often rely on low-level visual cues such as color, texture, or lightness to measure pixel similarity, making it difficult to achieve content-aware color propagation. While some recent approaches attempt to introduce semantic information into color editing, but often lead to unnatural, global color change in color adjustments. To overcome these limitations, we present a semantic palette-guided approach for color propagation. We first extract a semantic palette from an input image. Then, we solve an edited palette by minimizing a well-designed energy function based on user edits. Finally, local edits are accurately propagated to regions that share similar semantics via the solved palette. Our approach enables efficient yet accurate pixel-level color editing and ensures that local color changes are propagated in a content-aware manner. Extensive experiments demonstrated the effectiveness of our method.

Color grading, as a crucial step in film post-production, plays an important role in emotional expression and artistic enhancement. Recently, a geometric palette-based approach to video recoloring has been introduced with impressive results. It offers an intuitive interface that allows users to alter the color of a video by manipulating a limited set of representative colors. However, this method has two primary limitations. Firstly, palette extraction is computationally expensive, often taking more than one hour to generate palettes even for medium-length videos, which significantly limits the practical application of color editing for longer videos. Secondly, the palette colors are less representative, and some primary colors may be omitted from the resulting palettes during topological simplification, making it less intuitive in color editing. To overcome these limitations, in this paper, we propose a novel approach to video recoloring. The core of our method is a set of Bézier curves that connect the dominant colors throughout the input video. By slicing these Bézier curves in RGBT space, per-frame palette can be naturally derived. During recoloring, users can select several frames of interest and modify their corresponding palettes to change the color of the video. Our method is simple and intuitive, enabling compelling time-varying recoloring results. Compared to existing methods, our approach is more efficient in palette extraction and can effectively capture the dominant colors of the video. Extensive experiments demonstrate the effectiveness of our method.

A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and libraries. By layering regions filled with linear gradients, complex appearances can be created. We propose an automatic method to convert a raster image into layered regions of linear gradients. Given an input raster image segmented into regions, our approach decomposes the resulting regions into opaque and semi-transparent linear gradient fills. Our approach is fully automatic (e.g., users do not identify a background as in previous approaches) and exhaustively considers all possible decompositions that satisfy perceptual cues. Experiments on a variety of images demonstrate that our method is robust and effective.

Accurate camera pose estimation is essential and challenging for real world dynamic 3D reconstruction and augmented reality applications. In this article, we present a novel RGB-D SLAM approach for accurate camera pose tracking in dynamic environments. Previous methods detect dynamic components only across a short time-span of consecutive frames. Instead, we provide a more accurate dynamic 3D landmark detection method, followed by the use of long-term consistency via conditional random fields, which leverages long-term observations from multiple frames. Specifically, we first introduce an efficient initial camera pose estimation method based on distinguishing dynamic from static points using graph-cut RANSAC. These static/dynamic labels are used as priors for the unary potential in the conditional random fields, which further improves the accuracy of dynamic 3D landmark detection. Evaluation using the TUM and Bonn RGB-D dynamic datasets shows that our approach significantly outperforms state-of-the-art methods, providing much more accurate camera trajectory estimation in a variety of highly dynamic environments. We also show that dynamic 3D reconstruction can benefit from the camera poses estimated by our RGB-D SLAM approach.

Color correction and color grading are important steps in film production. Recent palette-based approaches to image recoloring have shown that a small set of representative colors provide an intuitive set of handles for color adjustment. However, a single, static palette cannot represent the time-varying colors in a video. We introduce a spatial-temporal geometry-based approach to video recoloring. Specifically, its core is a 4D skew polytope with a few vertices that approximately encloses the video pixels in color and time, which implicitly defines time-varying palettes through slicing of the 4D skew polytope at specific time values. Our geometric palette is compact, descriptive, and provides a correspondence between colors throughout the video, including topological changes when colors merge or split. Experiments show that our method produces natural, artifact-free recoloring.