Despite the remarkable progress in text-to-image generation models, AI-Generated Images (AGIs) still face significant challenges, such as poor perception quality and text-image misalignment. Consequently, AI-Generated Image Quality Assessment (AGIQA), which aims to assess capabilities of generative models, has gained increasing attention. Diffusion models, pre-trained on large-scale text-image generative tasks, inherently encode rich prior knowledge regarding image quality and text-image alignment, which is crucial for AGIQA task. Inspired by this, unlike most existing CLIP-based methods, we present an initial exploration of diffusion model-based AGIQA, termed Diff-AGIQA. Specifically, to effectively harness pre-trained diffusion models, we introduce a simple yet effective strategy focusing on two key aspects: 1) feature selection, identifying the discriminative features within diffusion models; and 2) visual prompting, to extract more task-specific features while keeping the diffusion model frozen. Extensive experiments on AGIQA-1K, AGIQA-3K, AGIQA-20K, and RichHF-18K datasets demonstrate the potential of applying diffusion models to AGIQA task. Code is publicly available https://github.com/sxfly99/Diff-AGIQA.

Aesthetic Image Cropping (AIC) aims to improve the visual appeal of images by removing redundant content while preserving attractive elements. Despite the encouraging progresses achieved in data-driven approaches, most existing models struggle to understand user intentions, particularly for diversified scenes with multiple subjects. Moreover, they can only provide cropping results without explanations, which further restricts their usability in real-world applications. Motivated by the above facts, we introduce InstructCrop : a multimodal large language model (MLLM)-based AIC framework, which can understand user instructions and provide explanatory reasons for cropping results. Specifically, we first build a multimodal Image Cropping Instruction Tuning (ICIT) dataset through a cost-effective paradigm by generating high-quality instruction tuning data based on the existing cropping datasets. Then, we embed dynamic domain knowledge into the cropping model by integrating cropping-aware experts of aesthetic assessment and composition classification. Finally, we adapt MLLMs to generate the cropping results and corresponding explanations. Quantitative and qualitative experiments on three benchmark datasets demonstrate that InstructCrop enables effective and interpretable image cropping, which aligns better with user intentions. Data and code are available at https://github.com/sxfly99/InstructCrop.

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, Radam, Adabound, and AdaBelief.

For taking out the adjustment process of sparse auto-encoder for broad learning system, Fenget al.proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi–Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this article builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST, and medical MNIST datasets are performed to show the improvement in testing accuracy.

Deep learning suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, an alternative learning framework with a non-iterative training mechanism named Broad Learning System (BLS) was proposed, which randomly assigns the parameters of hidden nodes and frozen them throughout the training process and then obtains its output weights using the ridge regression theory. This training method makes BLS have very high training efficiency. However, using ridge regression to solve the output weights cannot guarantee the stability of the solution in many cases, especially when the number of training samples is large, which may cause over-fitting and instability of BLS models. To solve this problem, we propose an improved BLS with a dense architecture and use the Proportional-Integral-Differential (PID) and Adaptive moment estimation (Adam) to replace the ridge regression operation. The new algorithm is called PID-A-DBLS, and its advantages include: 1) dense architecture can improve the feature extraction ability of the model; 2) using PID and Adam to solve the output weights can avoid the disadvantages of ridge regression. Extensive experimental results on four benchmark data sets show that PID-A-DBLS can achieve much better generalization ability and stability than BLS and its variants.

Conventional training mechanism for deep learning, which is based on gradient descent, suffers from many notorious issues such as low convergence rate, over-fitting, and time-consuming. To alleviate these problems, a novel deep learning algorithm with a different learning mechanism named Broad Learning System (BLS) was proposed by Prof. C. L. Philip Chen in 2017. BLS randomly selects the parameters of the feature nodes and enhancement nodes during its training process and uses the ridge regression theory to solve its output weights. BLS has been widely used in many fields because of its high efficiency. However, there is a fundamental problem that has not yet been solved, that is, the appropriate value of the parameter λ for the ridge regression operation of BLS is difficult to be set properly, which often leads to the problem of over-fitting and seriously limits the development of BLS. To solve this problem, we proposed a novel Dense BLS based on Conjugate Gradient (CG-DBLS) in this paper, in which each feature node is connected to other feature nodes and each enhancement node is connected to other enhancement nodes in a feed-forward fashion. The recursive least square method and conjugate gradient method are used to calculate the output weights of the feature nodes and enhancement nodes respectively. Experiment studies on four benchmark regression problems from UCI repository show that CG-DBLS can achieve much lower error and much higher stability than BLS and its variants.

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram-Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.