The Reverse 𝑘-Nearest Neighbor (R𝑘NN) query over moving objects on road networks seeks to find all moving objects that consider the specified query point as one of their 𝑘 nearest neighbors.In location based services, many users probably submit R𝑘NN queries simultaneously.However, existing methods largely overlook how to efficiently process multiple such queries together, missing opportunities to share redundant computations and thus reduce overall processing costs.To address this, this work is the first to explore batch processing of multiple R𝑘NN queries, aiming to minimize total computation by sharing duplicate calculations across queries.To tackle this issue, we propose the BR𝑘NN-Light algorithm, which uses rapid verification and pruning strategies based on geometric constraints, along with an optimized range search technique, to speed up the process of identifying the R𝑘NNs for each query.Furthermore, it proposes a dynamic distance caching mechanism to enable computation reuse when handling multiple queries, thereby significantly reducing unnecessary computations.Experiments on multiple real-world road networks demonstrate the superiority of the BR𝑘NN-Light algorithm on the processing of batch queries. CCS Concepts• Theory of computation → Database query processing and optimization (theory); Data structures and algorithms for data management.

The key challenge of multi-view indoor 3D object detection is to infer accurate geometry information from images for precise 3D detection. Previous method relies on NeRF for geometry reasoning. However, the geometry extracted from NeRF is generally inaccurate, which leads to sub-optimal detection performance. In this paper, we propose MVSDet which utilizes plane sweep for geometry-aware 3D object detection. To circumvent the requirement for a large number of depth planes for accurate depth prediction, we design a probabilistic sampling and soft weighting mechanism to decide the placement of pixel features on the 3D volume. We select multiple locations that score top in the probability volume for each pixel and use their probability score to indicate the confidence. We further apply recent pixel-aligned Gaussian Splatting to regularize depth prediction and improve detection performance with little computation overhead. Extensive experiments on ScanNet and ARKitScenes datasets are conducted to show the superiority of our model. Our code is available at https://github.com/Pixie8888/MVSDet.

Existing works on weakly-supervised audio-visual video parsing adopt hybrid attention network (HAN) as the multi-modal embedding to capture the cross-modal context. It embeds the audio and visual modalities with a shared network, where the cross-attention is performed at the input. However, such an early fusion method highly entangles the two non-fully correlated modalities and leads to sub-optimal performance in detecting single-modality events. To deal with this problem, we propose the messenger-guided mid-fusion transformer to reduce the uncorrelated cross-modal context in the fusion. The messengers condense the full cross-modal context into a compact representation to only preserve useful cross-modal information. Furthermore, due to the fact that microphones capture audio events from all directions, while cameras only record visual events within a restricted field of view, there is a more frequent occurrence of unaligned cross-modal context from audio for visual event predictions. We thus propose cross-audio prediction consistency to suppress the impact of irrelevant audio information on visual event prediction. Experiments consistently illustrate the superior performance of our framework compared to existing state-of-the-art methods.

Existing fully-supervised point cloud segmentation methods suffer in the dynamic testing environment with emerging new classes. Few-shot point cloud segmentation algorithms address this problem by learning to adapt to new classes at the sacrifice of segmentation accuracy for the base classes, which severely impedes its practicality. This largely motivates us to present the first attempt at a more practical paradigm of generalized few-shot point cloud segmentation, which requires the model to generalize to new categories with only a few support point clouds and simultaneously retain the capability to segment base classes. We propose the geometric words to represent geometric components shared between the base and novel classes, and incorporate them into a novel geometric-aware semantic representation to facilitate better generalization to the new classes without forgetting the old ones. Moreover, we introduce geometric prototypes to guide the segmentation with geometric prior knowledge. Extensive experiments on S3DIS and ScanNet consistently illustrate the superior performance of our method over baseline methods. Our code is available at: https://github.com/Pixie8888/GFS-3DSeg_GWs.

To effectively detect anomalies and eliminate the influence of noise on anomaly detection (AD), we propose a hyperspectral AD method based on low-rank decomposition and morphological filtering (LRDMF). For one thing, given the different ways in which anomalies and noise occur in the spectral bands, a low-rank decomposition model is proposed to decompose the original hyperspectral image (HSI) into the background, anomaly, and noise components, where a superpixel segmentation method and the sparse representation (SR) model are used to construct a robust background dictionary. For another thing, considering that the anomalies in HSI possess small area characteristics, a morphological filtering method is applied to preserve the small connected components. Finally, anomalies are detected by jointly considering the LRDMF results. The experimental results conducted on two real hyperspectral datasets demonstrate that the proposed method outperforms some of the state-of-the-art methods.

Methods of rain removal based on deep learning have rapidly developed, and the image quality after rain removal is continuously improving. However, the results of most methods have some common problems, including a loss of details, a blurring of edges, and the existence of artifacts. To remove rain-related information more thoroughly and retain more edge details, this paper proposes an end-to-end rain removal network based on the progressive residual detail supplement (ERRN-PRDS) approach. The entire network structure is designed in an iterative manner to obtain higher-quality rain removal images from coarse to fine. In the network, a diamond residual block is constructed as the main module of iteration to learn the feature information of the background layer. Meanwhile, to keep more texture details in the background layer, a detail supplement mechanism is designed between the iterative layers to transfer more information to the next iterative operation. Experimental results show that this method can remove the rain information more completely and better retain the image edges compared with previous state-of-the-art methods. In addition, because of the sparsity of the detail injection, our network also achieves high-quality results for image denoising tasks.