Automatic colorization techniques often struggle with dual-character line art, particularly in areas such as color coordination between characters, handling complex scenes and interactions, and meeting personalized colorization requirements. To address these challenges, we introduce DuCol, a novel framework specifically designed for the colorization of dual-character line art. DuCol leverages text-based inputs to accommodate personalized color preferences and integrates a Text-Labeled Adaptive Colorization (TAC) Module to ensure global color assignment, effectively harmonizing colors between characters. Furthermore, the model utilizes detailed segmentation information from a skeleton graph to enable precise boundary detection, resolving interactions between characters and preventing color bleeding or ambiguity. Extensive experiments on a large-scale illustration dataset demonstrate that DuCol’s superiority in dual-character line art colorization, establishing it as a leading solution in this domain.

Prompt learning is a common strategy for adapting Visual Language Models (VLMs) to downstream tasks by fine-tuning prompts for task-specific performance. However, existing methods face two key challenges: overfitting to base classes, which limits generalization to novel classes, and the dependence on manually generated or LLM-based descriptions, which are time-consuming and error-prone. To address these issues, we propose the Learning Hierarchical Attribute Prompt (LHAP) method, which introduces fine-grained semantic alignment through hierarchical prompts. By autonomously extracting visual attributes from images, LHAP generates local-level prompts (LLP) to capture fine-grained semantics and global-level prompts (GLP) to model overall semantics. The combination of LLP and GLP not only improves generalization but also mitigates errors and inefficiencies from manual or LLM-based descriptions. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and robustness of LHAP over state-of-the-art methods.

Class-incremental learning (CIL) enables models to learn new tasks without forgetting previously acquired knowledge. However, existing CIL approaches often struggle with inadequate adaptation to task-specific feature spaces and catastrophic forgetting of previously-acquired knowledge, compromising the models’ plasticity and stability. To address these challenges, this paper proposes a novel differential optimization paradigm called DO-CIL, which incorporates task-agnostic slow learner (TSL) with task-specific fast adapter (TFA) for rehearsal-free CIL. Specifically, TSL aims to effectively capture shared knowledge with low learning rates for robust generalization, while TFA allows pre-trained models to adapt to new task-specific feature spaces. Benefitting from the classifier retraining strategy, a learnable semantic shift network is also proposed to align prototypes with the evolving model representation, facilitating the retraining of task-specific classifiers based on these updated prototypes. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and effectiveness of our DO-CIL approach compared to state-of-the-art baselines.

Few-Shot Class-Incremental Learning (FSCIL) aims to incrementally learn new class knowledge from limited samples while preserving previously knowledge from encountered classes. However, existing FSCIL methods encounter two primary challenges: (1) inadequate adaptation, where overfitting to new classes compromises the model’s adaptability, and (2) catastrophic forgetting, where previously learned knowledge is not well preserved. In this paper, we propose the Virtual Prototype Guidance Prompting (VPGP) paradigm, integrating the Multi-Grained Prompt (MGP) and Virtual-Prototype Guidance (VPG) strategies. Specifically, MGP enhances adaptation and prevents overfitting by introducing domain-general and fine-grained prompts, expanding the embedding space to capture core feature representations of novel classes. Meanwhile, VPG mitigates catastrophic forgetting by employing a dynamic fusion strategy to retrieve robust old class knowledge and generate virtual prototype, guiding the model to maintain learned knowledge across different sessions. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our proposed VPGP framework.

Cross-Domain Few-Shot Learning (CD-FSL) aims to recognize unseen classes from target domains using only limited labeled samples. However, mainstream CD-FSL methods face two key challenges: (1) domain gap, arising from distributional differences between the source and target domains, and (2) overfitting, which occurs due to the small number of labeled samples in target domains, causing the model to overfit to these few samples. To address these challenges, we propose a novel Task-Aware Knowledge Prompt and Distillation (TKPD) method for CD-FSL, which integrates the Vision-Text Domain Prompt (VTDP) and Attribute-Task Knowledge Distillation (ATKD) modules. VTDP mitigates the domain gap by generating domain prompts to acquire domain-relevant knowledge, while ATKD strengthens the model by incorporating both homologous and heterogeneous knowledge, extending the knowledge base beyond the limited labeled samples and mitigating overfitting. Extensive experiments on 13 benchmark datasets validate the effectiveness of the proposed TKPD method.

Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge acquired from a source domain with abundant data to the target domain with limited labeled samples. Recent advancements have enhanced model generalization through Perturbation Augmentation (PA), facilitating more effective knowledge transfer. However, PA-based CD-FSL methods still suffer from two critical challenges, i.e., (1) limited diversity of augmented samples, making it difficult to cover the true distribution of unseen domains, and (2) conflicting gradients during model optimization, where augmented and original samples drive the model's optimization in opposing directions. To address these issues, we propose a novel PA-based framework with Style-Decoupled Augmentation (SDA) and Gradient-Conflict Adjustment (GCA) for Cross-Domain Few-Shot Learning, which is termed ''SG-FSL''. Specifically, SDA decouples the source domain style into style weights and basis styles, generating diverse unseen styles by perturbing the style weights to reweight the basis styles. Meanwhile, GCA leverages the angular relationships between the domain-specific gradient directions of augmented and original features, adaptively adjusting the gradient directions of original features to ensure that the model acquires diverse domain knowledge without interference, guiding it toward conflict-free optimization. Comprehensive experiments on multiple benchmark datasets consistently demonstrate the effectiveness and superiority of our method over state-of-the-art baselines.

Cyclic vertex connectivity cκ and cyclic edge connectivity cλ are two important kinds of conditional connectivity, which reflect the number of vertices or edges that can be removed before the graph is disconnected and at least two components contain a cycle, respectively. They have important applications in various networks such as computer networks or biochemical networks. In addition, a fullerene is a special kind of molecule in chemistry. A classic fullerene graph is a 3-connected cubic planar graph with only pentagonal and hexagonal faces. (4, 5, 6)-fullerene graphs are atypical fullerene graphs which also contain 4-faces. In this paper, we prove that cκ=cλ for (4,5,6)-fullerene graphs except for four exceptional graphs with order less than 16. We also give O(ν)-algorithms to determine the cyclic vertex connectivity and the cyclic edge connectivity of (4,5,6)-fullerene graphs.

With the rapid growth and widespread use of low-end commercial unmanned aerial vehicles (UAVs), it is critical to develop an object detection system that works well with these devices. This paper designs an efficient and lightweight object detection model specifically designed for low-end UAVs. Through excellent information interaction and the refined use of some techniques, our model can obtain multi-scale features and better focus on the details of different parts. Extensive experiments show that our model still maintains comparable accuracy while it consumes fewer parameters and FLOPs. In addition, our model has been applied to the tracking system of low-end UAVs, greatly enhancing the tracking performance.

Multi-label image recognition is a basic and challenging task in computer vision and multimedia fields. Graph Convolutional Networks (GCNs) are often used to learn the multi-label semantic features and multi-label dependency. Although the label semantic features in GCNs can learn the global image visual representation well, they are rarely used on the local image regions. Therefore, we try to use GCNs to learn global and local features at the same time, and make a balance between them. In this paper, we give a multi-scale semantic attention model MS-SGA-GCN including three main modules (i.e., MS, SGA and GCN) for multi-label image recognition. The Multi-Scale module (MS) utilizes feature maps of different sizes to obtain global features and have strong generalization capabilities. Semantic Guide Attention module (SGA) applies the label embeddings learned by GCNs to guide the generation of the cross-modality class-specific attention maps, which can discover the locations of semantically related regions for each label. Experiments show that our model on two datasets MS-COCO and PASCAL VOC2007 separately achieves the classification accuracy by 83.4% and 94.2%, which has a competitive advantage over other mainstream models.