Frequency-modulated continuous-wave radar is a cornerstone of advanced driver assistance systems thanks to its low cost and resilience to adverse weather. Yet the absence of explicit semantics makes radar annotation difficult, and the scarcity of large-scale labeled data limits the performance of radar perception models. To address this issue, we propose a self-supervised framework for object detection directly from Range–Azimuth– Doppler (RAD) cubes that learns transferable representations from unlabeled radar data. Specifically, we introduce cross-view contrastive learning to model correspondences among complementary views of the RAD cube, encouraging the network to capture spatial structure from multiple perspectives. In addition, an auxiliary cross-modal contrastive objective distills semantic knowledge from vision into radar. The joint objective integrates cross-view and cross-modal signals to strengthen radar feature representations. We further extend the framework to cross-domain pretraining using datasets from different sources. Experimental results demonstrate that the proposed method significantly improves radar object detection performance, especially with limited labeled data.

Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama. To address this issue and also the distortion of equirectangular projection in the panorama, we propose Cubemap Vision Transformers (CViT), a new transformer-based architecture that can model long-range dependencies and extract distortion-free global features from the panorama. We show that cubemap vision transformers have a global receptive field at every stage and can provide globally coherent predictions for spherical signals. As a general architecture, it removes any restriction that has been imposed on the panorama in many other monocular panoramic depth estimation methods. To preserve important local features, we further design a convolution-based branch in our pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision transformers at multiple scales. This global-to-local strategy allows us to fully exploit useful global and local features in the panorama, achieving state-of-the-art performance in panoramic depth estimation.

Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., L2distance) on the learned embeddings extracted by the teacher and student models. However, after using FCD, we observe that the intra-class similarities of the student model are lower than the intra-class similarities of the teacher model a lot. Therefore, we propose an effective FR distillation method called ICD-Face by introducing intra-class compactness distillation into the existing distillation framework. Specifically, in ICD-Face, we first propose to calculate the similarity distributions of the teacher and student models, where the feature banks are introduced to construct sufficient and high-quality positive pairs. Then, we estimate the probability distributions of the teacher and student models and introduce the Similarity Distribution Consistency (SDC) loss to improve the intra-class compactness of the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed ICD-Face for face recognition.

Within the field of face recognition (FR), it is widely accepted that the key objective is to optimize the entire feature space in the training process and acquire robust feature representations. However, most real-world FR systems tend to operate at a pre-defined False Accept Rate (FAR), and the corresponding True Accept Rate (TAR) represents the performance of the FR systems, which indicates that the optimization on the pre-defined FAR is more meaningful and important in the practical evaluation process. In this paper, we call the predefined FAR as Anchor FAR, and we argue that the existing FR loss functions cannot guarantee the optimal TAR under the Anchor FAR, which impedes further improvements of FR systems. To this end, we propose AnchorFace to bridge the aforementioned gap between the training and practical evaluation process for FR. Given the Anchor FAR, AnchorFace can boost the performance of FR systems by directly optimizing the non-differentiable FR evaluation metrics. Specifically, in AnchorFace, we first calculate the similarities of the positive and negative pairs based on both the features of the current batch and the stored features in the maintained online-updating set. Then, we generate the differentiable TAR loss and FAR loss using a soften strategy. Our AnchorFace can be readily integrated into most existing FR loss functions, and extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of AnchorFace.

Deep neural networks have achieved impressive success on various multimedia applications in the past decades. To reach a higher performance on real-world resource-constrained devices with large models that are already learned, knowledge distillation, which aims at transferring representational knowledge from a large teacher network into a small student network, has attracted increasing attention. Recently, contrastive distillation methods have achieved superior performance in this area, due to the powerful representability brought by contrastive/self-supervised learning. These models often transfer knowledge through individual samples or inter-class relationships, while ignoring the correlation lying among intra-class samples, which convey abundant information. In this paper, we propose a Positive pair Aware Contrastive Knowledge Distillation (PACKD) framework to extend the contrastive distillation with more positive pairs to capture more abundant knowledge from the teacher. Specifically, it pulls together features of pairs from the same class learned by the student and teacher while simultaneously pushing apart those of pairs from different classes. With a positive-pair similarity weighting strategy based on optimal transport, the proposed contrastive objective is able to improve the feature discriminability between positive samples with large visual discrepancies. Experiments on different benchmarks demonstrate the effectiveness of the proposed PACKD.

In this paper, we raise a new problem, namely cross model face recognition (CMFR), which has considerable economic and social significance. The core of this problem is to make features extracted from different models comparable. However, the diversity, mainly caused by different application scenarios, frequent version updating, and all sorts of service platforms, obstructs interaction among different models and poses a great challenge. To solve this problem, from the perspective of Bayesian modelling, we propose R3Adversarial Network (R3AN) which consists of three paths: Reconstruction, Representation and Regression. We also introduce adversarial learning into the reconstruction path for better performance. Comprehensive experiments on public datasets demonstrate the feasibility of interaction among different models with the proposed framework. When updating the gallery, R3AN conducts the feature transformation nearly 10 times faster than ResNet-101. Meanwhile, the transformed feature distribution is very close to that of target model, and its error rate is incredibly reduced by approximately 75% compared with a naive transformation model. Furthermore, we show that face feature can be deciphered into original face image roughly by the reconstruction path, which may give valuable hints for improving the original face recognition models.

This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network. Different from forwarding through different blocks sequentially in previous networks, we demonstrate that the DRNN can achieve better performance with fewer blocks by employing block recursively. We further add a gate structure to each block, which can adaptively decide the loop times of recursive blocks to reduce the computational cost. Since the recursive networks are hard to train, we propose the Loopy Variable Batch Normalization (LVBN) to stabilize the volatile gradient. Further, we improve the LVBN to correct statistical bias caused by the gate structure. Experiments show that the DRNN reduces the parameters and computational cost and while outperforms the original model in term of the accuracy consistently on CIFAR-10 and ImageNet-1k. Lastly we visualize and discuss the relation between image saliency and the number of loop time.