The proliferation of synthetic facial imagery has intensified the need for robust Open-World DeepFake Attribution (OW-DFA), which aims to attribute both known and unknown forgeries using labeled data for known types and unlabeled data containing a mixture of known and novel types. However, existing OW-DFA methods face two critical limitations: 1) A confidence skew that leads to unreliable pseudo-labels for novel forgeries, resulting in biased training. 2) An unrealistic assumption that the number of unknown forgery types is known a priori. To address these challenges, we propose a Confidence-aware Asymmetric Learning (CAL) framework, which adaptively balances model confidence across known and novel forgery types. CAL mainly consists of two components: Confidence-aware Consistency Regularization (CCR) and Asymmetric Confidence Reinforcement (ACR). CCR mitigates pseudo-label bias by dynamically scaling sample losses based on normalized confidence, gradually shifting the training focus from high- to low-confidence samples. ACR complements this by separately calibrating confidence for known and novel classes through selective learning on high-confidence samples, guided by their confidence gap. Together, CCR and ACR form a mutually reinforcing loop that significantly improves the model's OW-DFA performance. Moreover, we introduce a Dynamic Prototype Pruning (DPP) strategy that automatically estimates the number of novel forgery types in a coarse-to-fine manner, removing the need for unrealistic prior assumptions and enhancing the scalability of our methods to real-world OW-DFA scenarios. Extensive experiments on the standard and OW-DFA benchmark and a newly extended benchmark incorporating advanced manipulations demonstrate that CAL consistently outperforms previous methods, achieving new state-of-the-art performance on both known and novel forgery attribution.

In this paper, we investigate a practical yet challenging task: On-the-fly Category Discovery (OCD). This task focuses on the online identification of newly arriving stream data that may belong to both known and unknown categories, utilizing the category knowledge from only labeled data. Existing OCD methods are devoted to fully mining transferable knowledge from only labeled data. However, the transferability learned by these methods is limited because the knowledge contained in known categories is often insufficient, especially when few annotated data/categories are available in fine-grained recognition. To mitigate this limitation, we propose a diffusion-based OCD framework, dubbed DiffGRE, which integrates Generation, Refinement, and Encoding in a multi-stage fashion. Specifically, we first design an attribute-composition generation method based on cross-image interpolation in the diffusion latent space to synthesize novel samples. Then, we propose a diversity-driven refinement approach to select the synthesized images that differ from known categories for subsequent OCD model training. Finally, we leverage a semi-supervised leader encoding to inject additional category knowledge contained in synthesized data into the OCD models, which can benefit the discovery of both known and unknown categories during the on-the-fly inference process. Extensive experiments demonstrate the superiority of our DiffGRE over previous methods on six fine-grained datasets.

Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignment jointly, surpasses the performance of traditional ones on image data. However, existing methods rarely consider the role of model learning strategies in improving the robustness and performance of clustering complex image data. Furthermore, most approaches rely solely on point-to-point distances to cluster centers for partitioning the latent representations, resulting in error accumulation throughout the iterative process. In this paper, we propose a robust image clustering method (IDCL) which, to our knowledge for the first time, introduces a model training strategy using density information into image clustering. Specifically, we design a curriculum learning scheme grounded in the density information of input data, with a more reasonable learning pace. Moreover, we employ the density core rather than the individual cluster center to guide the cluster assignment. Finally, extensive comparisons with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method, including robustness, rapid convergence, and flexibility in terms of data scale, number of clusters, and image context.

In this paper, we study a practical yet challenging task, On-the-fly Category Discovery (OCD), aiming to online discover the newly-coming stream data that belong to both known and unknown classes, by leveraging only known category knowledge contained in labeled data. Previous OCD methods employ the hash-based technique to represent old/new categories by hash codes for instance-wise inference. However, directly mapping features into low-dimensional hash space not only inevitably damages the ability to distinguish classes and but also causes ``high sensitivity'' issue, especially for fine-grained classes, leading to inferior performance. To address these drawbacks, we propose a novel Prototypical Hash Encoding (PHE) framework consisting of Category-aware Prototype Generation (CPG) and Discriminative Category Encoding (DCE) to mitigate the sensitivity of hash code while preserving rich discriminative information contained in high-dimension feature space, in a two-stage projection fashion. CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes. DCE boosts the discrimination ability of hash code with the guidance of the generated category prototypes and the constraint of minimum separation distance. By jointly optimizing CPG and DCE, we demonstrate that these two components are mutually beneficial towards an effective OCD. Extensive experiments show the significant superiority of our PHE over previous methods, e.g. obtaining an improvement of +5.3% in ALL ACC averaged on all datasets. Moreover, due to the nature of the interpretable prototypes, we visually analyze the underlying mechanism of how PHE helps group certain samples into either known or unknown categories. Code is available at https://github.com/HaiyangZheng/PHE.

Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensional image data. Unfortunately, existing deep clustering methods (DC) often ignore the importance of information fusion with a global perception field among different image regions for clustering images, especially complex ones. Additionally, the learned features are usually not clustering-friendly in terms of dimensionality and are based only on simple distance information for the clustering. In this regard, we propose a deep embedded image clustering TDEC, which for the first time to our knowledge, jointly considers feature representation, dimensional preference, and robust assignment for image clustering. Specifically, we introduce the Transformer to form a novel module T-Encoder to learn discriminative features with global dependency while using the Dim-Reduction block to build a friendly low-dimensional space favoring clustering. Moreover, the distribution information of embedded features is considered in the clustering process to provide reliable supervised signals for joint training. Our method is robust and allows for more flexibility in data size, the number of clusters, and the context complexity. More importantly, the clustering performance of TDEC is much higher than that of recent competitors. Extensive experiments with state-of-the-art approaches on complex datasets demonstrate the superiority of TDEC.

Service uncertainty modeling is an important problem of manufacturing service composition optimization, this article proposes a cloud manufacturing service composition optimization framework based on cloud-edge collaboration considering manufacturing service uncertainty. In the proposed framework, on the edge side, a model parameters estimation method of the manufacturing services' uncertainty is proposed based on Gaussian mixture regression; while on the cloud side, an intelligent evolutionary algorithm is adopted to effectively optimize the manufacturing service composition. Since the Gaussian mixture distribution is used to approximate the service availability distribution, the service uncertainty can be modeled adaptively. Compared with the previous optimization methods of manufacturing service composition with uncertainty based on the deterministic parameter models, the method proposed in this article can model the uncertainty of service more effectively, thus obtain better service composition solutions. Extensive experimental results prove the effectiveness of the algorithm.

This paper investigates the access time of mobile nodes to infrastructure networks in Mobile Hybrid Networks (MHNs), e.g., sensor-actuator networks, where mobile nodes move around the coexisting infrastructure networks. In such networks, mobile nodes may relay data packets in a hop-by-hop fashion, and eventually deliver packets to the wired networks. Our objective is to study the lower bound of the access time, which implies the minimum time to deliver data packets from low-speed, unreliable wireless networks to the high-speed, reliable infrastructure network. In particular, we propose a theoretical framework to analyze the packet propagation speed in MHNs, and to show that there is a unified lower bound on the access time regardless of data forwarding schemes. When wireless nodes move at an average speed of v and variance σ2towards an AP, and their density λ is small (e.g., the network is sparse and surely disconnected), we prove that the expected access time is lower bounded by (Lb(0)-r)2/σ2packet mobility is unbiased and Lb(0)-r/v for the biased case, where Lb(0) is the distance from the source node to the nearest access point. Here, packet mobility is the mobility of a packet due to both node mobility and data transmissions. We further propose a routing scheme to achieve such theoretical bounds.

This paper gives a semantics for discrete-event (DE) models that generalizes that of synchronous/reactive (SR) languages, and a continuous-time (CT) semantics that generalizes the DE semantics. It shows that all three semantic models can be used in actor-oriented composition languages, and that despite the fact that CT is the most general, there are good reasons for using each of the more specialized semantics. Moreover, because of the generalization relationship between them, these three models of computation (MoCs) compose hierarchically in arbitrary order. We describe a design system that supports arbitrary combinations of these three MoCs, leveraging the actor abstract semantics of Ptolemy II.