arXiv subjects

Ni Zhang

Publications and source records attributed to Ni Zhang.

23 records · Page 2Linked to original sources

arXiv2023

AGAD: Adversarial Generative Anomaly Detection

Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly detection methods are often used to solely leverage normal data to detect abnormalities that deviated from the learnt normality distributions. Meanwhile, given the fact that limited anomaly data can be obtained with a minor cost in practice, some researches also investigated anomaly detection methods under supervised scenarios with limited anomaly data. In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples. Essentially, our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios. Extensive experiments are carried out on multiple benchmark datasets and real-world datasets, the results show significant improvement in both supervised and semi-supervised scenarios. Importantly, our approach is data-efficient that can boost up the detection accuracy with no more than 5% anomalous training data.

cs.CV↗

arXiv2023

Photocurrent imaging of hybrid polaritons in graphene based heterostructures

Photocurrent is arising as a powerful tool for detecting in-plane collective excitations in hybrid polariton systems. In this paper, based on the intrinsic optoelectric response of graphene, photocurrent imaging of in-plane plasmons from each graphene layer is presented in a hybrid graphene-graphene heterostructure. In combination with near-field optical signals which detect plasmons above the sample, three dimensional detection of hybrid plasmons is demonstrated. Especially, only an electronic boundary is necessary for the electrical detection of hybrid plasmons, which acts as both the photocurrent junction and plasmon reflector. Our studies would promote electrical studies of polariton related physical phenomena and pave the way towards all-electrical nano-optical processing.

physics.optics↗

arXiv2021

Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection

Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usually lack robustness, scalability, or stability for the first process and simply fuse consensus features with image features for the second process. In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process. To summarize consensus image features, we first summarize robust features for every single image using an effective pooling method and then aggregate cross-image consensus cues via the self-attention mechanism. By doing this, our model meets the scalability and stability requirements. Next, we generate dynamic kernels from consensus features to encode the summarized consensus knowledge. Two kinds of kernels are generated in a supplementary way to summarize fine-grained image-specific consensus object cues and the coarse group-wise common knowledge, respectively. Then, we can effectively perform object searching by employing dynamic convolution at multiple scales. Besides, a novel and effective data synthesis method is also proposed to train our network. Experimental results on four benchmark datasets verify the effectiveness of our proposed method. Our code and saliency maps are available at \url{https://github.com/nnizhang/CADC}.

cs.CV↗

arXiv2021

Visual Saliency Transformer

Existing state-of-the-art saliency detection methods heavily rely on CNN-based architectures. Alternatively, we rethink this task from a convolution-free sequence-to-sequence perspective and predict saliency by modeling long-range dependencies, which can not be achieved by convolution. Specifically, we develop a novel unified model based on a pure transformer, namely, Visual Saliency Transformer (VST), for both RGB and RGB-D salient object detection (SOD). It takes image patches as inputs and leverages the transformer to propagate global contexts among image patches. Unlike conventional architectures used in Vision Transformer (ViT), we leverage multi-level token fusion and propose a new token upsampling method under the transformer framework to get high-resolution detection results. We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism. Experimental results show that our model outperforms existing methods on both RGB and RGB-D SOD benchmark datasets. Most importantly, our whole framework not only provides a new perspective for the SOD field but also shows a new paradigm for transformer-based dense prediction models. Code is available at https://github.com/nnizhang/VST.

cs.CV↗

arXiv2020

Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection

How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection. Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss. Many models use the feature fusion strategy but are limited by the low-order point-to-point fusion methods. In this paper, we propose a novel mutual attention model by fusing attention and contexts from different modalities. We use the non-local attention of one modality to propagate long-range contextual dependencies for the other modality, thus leveraging complementary attention cues to perform high-order and trilinear cross-modal interaction. We also propose to induce contrast inference from the mutual attention and obtain a unified model. Considering low-quality depth data may detriment the model performance, we further propose selective attention to reweight the added depth cues. We embed the proposed modules in a two-stream CNN for RGB-D SOD. Experimental results have demonstrated the effectiveness of our proposed model. Moreover, we also construct a new challenging large-scale RGB-D SOD dataset with high-quality, thus can both promote the training and evaluation of deep models.

cs.CV↗