Window-based Transformers have substantially improved the practicality of image restoration techniques by reducing computational costs, but their local attention mechanism introduces a bottleneck that limits further performance gains. Expanding the attention range holds promise for better image restoration quality. However, the expansion faces two fundamental issues: (1) prohibitive quadratic growth in computational complexity, and (2) increased attention noise in feature aggregation caused by the inclusion of more irrelevant pixels. In this paper, we propose RouteWinFormer, a novel window-based Transformer that enables wider-range attention in image restoration. RouteWinFormer incorporates the Route-Window Attention Module (RWAM) to efficiently extend attention to mid-range regions, which are identified as critical for image restoration according to an analysis of normalized average attention distances. RWAM adopts a router that dynamically selects a subset of nearby windows for aggregation, rather than including all neighboring windows, alleviating the growth in computational complexity. The selection leverages regional similarity based on a window-level description, reducing the inclusion of irrelevant pixels and attention noise during aggregation. In addition, we introduce Multi-Scale Structure Regularization (MSR) for RWAM to learn a better structure prior. MSR encourages the sub-scale branches of a U-shaped network to extract structural information, which serves as a condition for the original-scale branch to learn degradation patterns. Experimental results show that RouteWinFormer outperforms state-of-the-art methods on 8 benchmarks across diverse image restoration tasks, including defocus deblurring, desnowing, dehazing, deraining, and motion deblurring. The source code is available at: https://github.com/QifanLi-vilab/RouteWinFormer.

Personalizing the analysis for web search potentially improves the search experience. A good analytical model for web search should leverage not only collective wisdom but also individual characteristics. Most of the existing analytical models, however, focus on how to utilize the collective wisdom, from a crowd, for instance. In this paper, we address the problem of user-specific web search analysis by considering the so-called burstiness in web search, which captures the behavior of rare words appearing many times in a single document. We go beyond click graph and propose two probabilistic topic models, Topic Independence Model and Topic Dependence Model. The former adopts the assumption that the generation of query terms and URLs are topically independent, and the latter captures the coupling between search queries and URLs. We also capture the temporal burstiness of topics by utilizing continuous Beta distribution. Through a large-scale analysis of a real-life search query log, we observe that each user's web search trail enjoys multiple kinds of user-based unique characteristics. On a massive search query log, the new models achieve a better held-out likelihood than existing baselines, and they can also effectively reveal the latent evolution of topics on the corpus level and user-based level.

Since inaccuracies commonly exist in many applications, data uncertainty has become an important problem in database systems. To deal with data uncertainty, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with confidence. However, the results from a query or mining process may not be reliable when the uncertainty propagates in the systems. In this paper, we leverage the power of crowdsourcing by designing a set of Human Intelligence Tasks, or HITs in short, to ask a crowd to improve the quality of uncertain data. In particular, we consider crowds consists of workers with diverse accuracy rates when answering the HITs. We design solutions to maximize the data quality with minimal number of HITs. There are two obstacles for this non-trivial optimization, which lead to very high computational cost for selecting the optimal set of HITs. First, members of a crowd may return incorrect answers with different probabilities. Second, the HITs decomposed from uncertain data are often correlated. We have addressed these challenges in this paper by designing an effective approximation algorithm and an efficient heuristic solution, especially for crowds with diverse individual accuracy rates. To further improve the efficiency, we derive tight lower and upper bounds for effective filtering and estimation. Extensive experiments on both a simulated crowd and a real crowdsourcing platform are conducted to evaluate our solutions.

We introduce techniques enabling interactive guidance for better self-portrait photos ("selfies") using a smartphone cam- era. Aesthetic quality is estimated using empirical models for three parameterized composition principles: face size, face position, and lighting direction. The models are built using 2,700 crowdworker assessments of highly-controlled synthetic selfies. These are generated by manipulating a virtual camera and lighting when rendering a realistic 3D model of a human to methodically explore the parameter space. A camera application uses the models to estimate the aesthetic quality of a live selfie preview based on parameters measured by computer vision. The photographer is guided towards a better selfie by directional hints overlaid on the live preview. A study shows the technique provides a 26% increase in aesthetic quality compared to a standard camera application.