Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets.Knowledge scarcity and temporal lag create knowledge gaps for domain applications.While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations.Continual Pre-Training (CPT) treats all tokens in domain documents with equal importance, failing to prioritize critical knowledge points, while supervised fine-tuning (SFT) with question-answer pairs struggles to develop the coherent knowledge structures necessary for complex reasoning tasks.To address these challenges, we propose Reinforcement Learning from Augmented Generation (RLAG).Our approach iteratively cycles between sampling generations and optimizing the model through calculated rewards, effectively embedding critical and contextually coherent domain knowledge.We select generated outputs with the highest log probabilities as the sampling result, then compute three tailored reward metrics to guide the optimization process.To comprehensively evaluate domain expertise, we assess answer accuracy and the rationality of explanations generated for correctly answered questions.Experimental results across medical, legal, astronomy, and current events datasets demonstrate that our proposed method significantly outperforms baseline approaches.

No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model are publicly available on the project page https://kwanyeelin.github.io/projects/HIQA/HIQA.html.

A new texture classification method based on wavelet transform is presented. The elements of the signature vector, FDBC , of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the three‐level high‐frequency domains and the third low‐frequency domain. The pretreatment is done with SVD decomposition and reconstruction by dropping half singular values. The one‐nearest‐neighbour classifier (1NN) with distance is used to make the classification. Furthermore, to improve classification result, the classifier 1NN is strengthened with weighted distance. The proposed method is tested on five subsets from Brodatz database and UMD database and is experimentally proved more efficient and more promising.