Traditional person re-identification (re-id) methods perform poorly under changing illuminations. This situation can be addressed by using dual-cameras that capture visible images in a bright environment and infrared images in a dark environment. Yet, this scheme needs to solve the visible-infrared matching issue, which is largely under-studied. Matching pedestrians across heterogeneous modalities is extremely challenging because of different visual characteristics. In this paper, we propose a novel framework that employ modality-specific networks to tackle with the heterogeneous matching problem. The proposed framework utilizes the modality-related information and extracts modality-specific representations (MSR) by constructing an individual network for each modality. In addition, a cross-modality Euclidean constraint is introduced to narrow the gap between different networks. We also integrate the modality-shared layers into modality-specific networks to extract shareable information and use a modality-shared identity loss to facilitate the extraction of modality-invariant features. Then a modality-specific discriminant metric is learned for each domain to strengthen the discriminative power of MSR. Eventually, we use a view classifier to learn view information. The experiments demonstrate that the MSR effectively improves the performance of deep networks on VI-REID and remarkably outperforms the state-of-the-art methods.

RGB-Infrared (RGB-IR) cross-modality person re-identification (re-ID) is attracting more and more attention due to requirements for 24-h scene surveillance. However, the high cost of labeling person identities of an RGB-IR dataset largely limits the scalability of supervised models in real-world scenarios. In this paper, we study the unsupervised RGB-IR person re-ID problem (or briefly uRGB-IR re-ID) in which no identity annotations are available in RGB-IR cross-modality datasets. Considering that intra-modality (i.e., RGB-RGB or IR-IR) re-ID is much easier than cross-modality re-ID and can provide shared knowledge for RGB-IR re-ID, we propose a two-stage method to solve the uRGB-IR re-ID, namely homogeneous-to-heterogeneous learning. In the first stage, the unsupervised self-learning method is conducted to learn the intra-modality feature representation and to generate the pseudo-labeled identities of person images separately for each modality. In the second stage, heterogeneous learning is used to learn a shared discriminative feature representation by distilling the knowledge from intra-modality pseudo-labels, to align two modalities via a modality-based consistent learning module, and finally to target modality-invariant learning via a pseudo-labeled positive instance selection module. With the use of homogeneous-to-heterogeneous learning, the proposed unsupervised framework greatly reduces the modality gap and thus learns a robust feature representation against RGB and infrared modalities, leading to promising accuracy. We also propose a novel cross-modality re-ranking approach that includes a self-modality search and a cycle-modality search to tailor the uRGB-IR re-ID. Unlike conventional re-ranking, the proposed re-ranking method takes a modality-based constraint into re-ranking and thus can select more reliable nearest neighbors, which greatly improves uRGB-IR re-ID. The experimental results demonstrate the superiority of our approach on the SYSU-MM01 and RegDB datasets.

A face image can be represented by a combination of large-and small-scale features. It is well-known that the variations of illumination mainly affect the large-scale features (low-frequency components), and not so much the small-scale features. Therefore, in relevant existing methods only the small-scale features are extracted as illumination-invariant features for face recognition, while the large-scale intrinsic features are always ignored. In this paper, we argue that both large-and small-scale features of a face image are important for face restoration and recognition. Moreover, we suggest that illumination normalization should be performed mainly on the large-scale features of a face image rather than on the original face image. A novel method of normalizing both the Small-and Large-scale (S&L) features of a face image is proposed. In this method, a single face image is first decomposed into large-and small-scale features. After that, illumination normalization is mainly performed on the large-scale features, and only a minor correction is made on the small-scale features. Finally, a normalized face image is generated by combining the processed large-and small-scale features. In addition, an optional visual compensation step is suggested for improving the visual quality of the normalized image. Experiments on CMU-PIE, Extended Yale B, and FRGC 2.0 face databases show that by using the proposed method significantly better recognition performance and visual results can be obtained as compared to related state-of-the-art methods.

We present a learning model that makes full use of boundary information for salient object segmentation. Specifically, we come up with a novel loss function, i.e., Contour Loss, which leverages object contours to guide models to perceive salient object boundaries. Such a boundary-aware network can learn boundary-wise distinctions between salient objects and background, hence effectively facilitating the salient object segmentation. Yet the Contour Loss emphasizes the boundaries to capture the contextual details in the local range. We further propose the hierarchical global attention module (HGAM), which forces the model hierarchically to attend to global contexts, thus captures the global visual saliency. Comprehensive experiments on six benchmark datasets show that our method achieves superior performance over state-of-the-art ones. Moreover, our model has a real-time speed of 26 fps on a TITAN X GPU.

DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works mainly focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model based on face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models. Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. We also present a novel post-regularization module for enhancing the transferability of generated adversarial examples. To comprehensively measure the effectiveness of our approaches, we construct a challenging baseline of DeepFake adversarial attacks for future development. Extensive experiments impressively show that the proposed adversarial attack method makes the visual quality of DeepFake face images plummet so that they are easier to be detected by humans and algorithms. Moreover, we demonstrate that the proposed algorithm can be generalized to offer face image protection against various face translation methods.

In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of the re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which utilizes the view information in the feature extraction stage. The proposed framework learns a view-specific network for each camera view with a cross-view Euclidean constraint (CV-EC) and a cross-view center loss. We utilize the CV-EC to decrease the margin of the features between diverse views and extend the center loss metric to a view-specific version to better adapt the re-id problem. Moreover, we propose an iterative algorithm to optimize the parameters of the view-specific networks from coarse to fine. The experiments demonstrate that our approach significantly improves the performance of the existing deep networks and outperforms the state-of-the-art methods on the VIPeR, CUHK01, CUHK03, SYSU-mReId, and Market-1501 benchmarks.