Instance-level change detection in 3D scenes presents significant challenges, particularly under uncontrolled conditions without labeled image pairs, varying camera poses, or restricted lighting. This paper addresses this challenge by developing a novel approach to detect changes in real-world scenarios. Leveraging 4D Gaussians to embed multiple images into 3D Gaussian distributions, our method enables the rendering of two coherent image sequences. By segmenting each image and assigning a unique identifier to each instance, we can efficiently identify changed instances through ID comparison. Additionally, we utilize change maps and classification encodings to categorize the 4D Gaussians as changed or unchanged, allowing for the rendering of a comprehensive change map from any view direction. Through extensive experiments on various instance-level change detection datasets, our method demonstrates significant improvements in detection accuracy over state-of-the-art methods like C-NERF and CYWS-3D, particularly in scenarios with large lighting variations.

Most existing change detection (CD) methods focus on spatial domain modeling, while ignore the rich information of frequency domain. In this paper, we enhance the feature representative ability with spatial and frequency feature cross enhancement. Specifically, we propose a Change Feature Extract Module (CFEM) to obtain high-quality change features from the bi-temporal images. These features are then merged together and refined through three parallel branches: the Local branch uses multiscale max-pooling operations to generate multiscale feature; the Wavelet Transform Decomposer (WTD) branch decomposes feature into low-frequency and high-frequency signals with Haar wavelet transform; the Global branch adopts Mamba to capture the long-range dependencies. To bridge the semantic gap between frequency and spatial features, we design Dual-Representation Aggregation Module (DRAM) to promote the combination of features from different representation domains in a flow-based manner and dual cross attention. Extensive experiments demonstrate our method outperforms 11 SOTA CD methods on three remote sensing CD datasets.

In this work, we aim to detect the changes caused by object variations in a scene represented by the neural radiance fields (NeRFs). Given an arbitrary view and two sets of scene images captured at different timestamps, we can predict scene changes in that view, which has significant potential applications in scene monitoring and measuring. We conducted preliminary studies and found that such an exciting task cannot be easily achieved by utilizing existing NeRFs and 2D change detection (CD) methods with many false or missing detections. The main reason is that the 2D CD is based on the pixel appearance difference between spatial-aligned image pairs and neglects the stereo information in the NeRF. To address the limitations, we propose the C-NeRF to represent scene changes as directional consistency difference-based NeRF, which mainly contains three modules. We first build two aligned NeRFs from pre-change and post-change scenes. Then, we identify the change points based on the direction-consistent constraint; that is, real change points have similar change representations across view directions, but fake change points do not. Finally, we design the change map rendering process based on the built NeRFs and can generate the change map of an arbitrarily specified view direction. To validate the effectiveness, we build a new dataset containing ten scenes covering diverse scenarios with different changing objects. Our approach surpasses state-of-the-art CD methods and NeRF-based methods by a significant margin.

Most existing backdoor attacks focus on designing various trigger injection methods and fine-tuning victim networks, which are difficult to deploy in real-world applications. In this paper, we propose a plug-and-play virus network, dubbed PAPnet, for backdoor attack. PAPnet is a lightweight network with the same dimensional output as the victim network. In the training stage, we only need the output of the victim network and train PAPnet to learn from poisoned data and clean data. This makes PAPnet easier to learn than fine-tunebased backdoor attack methods. Besides, PAPnet can be easily attached to the different classification network models without modifying the architecture of the victim network and fine-tuning processing. We have conducted various experiments on four datasets with four classical classification networks. Experimental results demonstrate the superiority of our proposed method.

Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales always results in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features. Our multiscale RGB-D saliency detector uses SEFF and processes images with three different scales. SEFF is used to fuse the features of RGB and depth images, as well as the features of decoders at different scales. Extensive experiments on five benchmark datasets have demonstrated the superiority of our method over ten SOTA saliency detectors.

Change detection (CD) aims at finding change objects from bi-temporal images, which has wide applications in different vision tasks. Previous CD methods focus more on fusing inter-layer multiscale features while ignoring the intra-layer multiscale characteristics, which hurts the integrity of change objects with different sizes. In this paper, we propose to mix intra- and inter-layer multiscale features to generate more complete change regions. To realize intra-layer multi-scale, we propose inception difference module (IDM), which employs convolutional filters with different sizes, absolute differences, and residual connections to capture intra-layer multiscale characteristics. To capture inter-layer multiscale, we propose a residual network refinement module (RNR) to fuse the features from the highest layer to the lowest layer and generate finely detailed change predictions. Our method can capture complete changes of different sizes by considering the multiscale characteristics of intra- and inter-layer simultaneously. Experiments on two benchmark datasets reveal that our method outperforms six state-of-the-art change detectors.