The issue of detecting face forgeries has garnered significant interest in the field of computer vision, primarily driven by the growing social concerns of indistinguishable deepfake images. One of the primary obstacles encountered in the field of deepfake detection is enhancing the discriminative power of learned features. In this paper, we propose a Concentric Ring Loss (CRL) that aims to promote the learning of compressed intra-class features and separated inter-class features inside a model. Specifically, we apply margin penalties in both Euclidean and angular space separately, which serve to increase the separation between real and fake images. Moreover, we introduce a frequency-aware triplet network with a self-developed sample generation strategy, which provides efficient hard triplets for model training. Extensive experiments demonstrate the superiority of our methods over multiple datasets. We show that CRL consistently outperforms the state-of-the-art by a large margin.

Analysis of indoor spaces requires topological information. In this paper, we propose to extract topological information from room attributes using what we call Iterative and adaptive graph Topology Learning (ITL). ITL progressively predicts multiple relations between rooms; at each iteration, it improves node embeddings, which in turn facilitates the generation of a better topological graph structure. This notion of iterative improvement of node embeddings and topological graph structure is in the same spirit as [5]. However, while [5] computes the adjacency matrix based on node similarity, we learn the graph metric using a relational decoder to extract room correlations. Experiments using a new challenging indoor dataset validate our proposed method. Qualitative and quantitative evaluation for layout topology prediction and floorplan generation applications also demonstrate the effectiveness of ITL.

Multi-modal time series classification (MTC) uses complementary information from different modalities to improve the learning performance. Obtaining informative modality-specific representation plays an essential role in MTC. Attention mechanism has been widely adopted as an effective strategy for discovering discriminative cues underlying temporal data. However, most existing MTC methods only utilize attention to balance the feature weights within or cross modalities but ignore digging latent patterns from mutual-support information in attention space. Specifically, the attention distributions are different for multiple modalities which are supportive and instructional with each other. To this end, we propose a collaborative attention mechanism (CAM) for MTC based on a novel perspective to utilize attention module. CAM detects the attention differences among multi-modal time series, and adaptively integrates different attention information to benefit each other. We extend the long short-term memory (LSTM) to a Mutual-Aid RNN (MAR) for multi-modal collaboration. CAM takes advantages of modality-specific attention to guide another modality and discover potential information which is hard to be explored by itself. It paves a novel way of employing attention to enhance the capacity of multi-modal representations. Extensive experiments on four multi-modal time series datasets illustrate the CAM effectiveness to improve the single-modal and also boost multi-modal performances.

Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA).UODA consists of a generator and two classifiers (i.e., the sourcescattering classifier and the target-clustering classifier), which are trained for contradictory purposes.The target-clustering classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence.Meanwhile, the source-scattering classifier is designed to scatter the source features to enhance the decision boundary's smoothness.Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features.This strategy can make the cross-domain features to be precisely aligned against the source bias simultaneously.Moreover, to overcome the model collapse through training, we progressively update the measurement of feature's distance and their representation via an adversarial training paradigm.Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the superiority of our approach over the state-of-the-art methods.

High-dimensional time series segmentation aims to segment a long temporal sequence into several short and meaningful subsequences. The high-dimensionality makes it challenging due to the complicated correlations among the sequential features. A large number of labeled data is required in existing supervised methods, and unsupervised methods mainly deploy clustering approaches, which are sensitive to outliers and hard to guarantee high performance. Also, most existing methods mainly rely on hand-craft features to deal with regular time series segmentation and achieve promising results. However, these approaches cannot effectively handle high-dimensional time series and will result in a high computational cost. In our work, we propose a novel unsupervised representation learning framework called Dual-Side Auto-Encoder (DSAE). It mainly focuses on high-dimensional time series segmentation by effectively capturing the temporal correlative patterns. Specifically, a single-to-multiple auto-encoder is designed to capture local sequential information. Besides, a long-shot distance encoding strategy is proposed. It aims to explicitly guide the learning process to obtain distinctive representations for segmentation. Furthermore, the long-short distance strategy is also executed in the decoded feature space, which implicitly directs the representation learning. Substantial experiments on six datasets illustrate the model effectiveness.