The rapid advancement of Text-guided Image Editing (TIE) enables image modifications through text prompts. However, current TIE models still struggle to balance image quality, editing alignment, and consistency with the original image, limiting their practical applications. Existing TIE evaluation benchmarks and metrics have limitations on scale or alignment with human perception. To this end, we introduce EBench-18K, the first large-scale image Editing Benchmark including 18K edited images with fine-grained human preference annotations for evaluating TIE. Specifically, EBench-18K includes 1,080 source images with corresponding editing prompts across 21 tasks, 18K+ edited images produced by 17 state-of-the-art TIE models, 55K+ mean opinion scores (MOSs) assessed from three evaluation dimensions, and 18K+ question-answering (QA) pairs. Based on EBench-18K, we employ outstanding LMMs to assess edited images, while the evaluation results, in turn, provide insights into assessing the alignment between the LMMs' understanding ability and human preferences. Then, we propose LMM4Edit, a LMM-based metric for evaluating image Editing models from perceptual quality, editing alignment, attribute preservation, and task-specific QA accuracy in an all-in-one manner. Extensive experiments show that LMM4Edit achieves outstanding performance and aligns well with human preference. Zero-shot validation on the other datasets also shows the generalization ability of our model. The dataset and code are available at https://github.com/IntMeGroup/LMM4Edit.

In recent years, the combination of knowledge graph (KG) and recommendation algorithms has achieved significant success. KG contains rich information to improve users and items representations as side information. Existing KG-aware recommendation methods have shown effectiveness in some cases. However, we contend these methods mainly focus on extracting structural semantic information from KG, while not fully exploring the collaborative semantic information (semantically similar information that may not be directly reflected in the graph structure). Considering the heterogeneity of KG, we design a novel method under the contrastive learning paradigm, named Knowledge Graph Recommendation based on Structural Semantics and Collaborative Semantics (KGSC). In this paper, we focus on exploring the integration of collaborative semantic information and studying the complementary relationship between structural semantics and collaborative semantics. Specifically, we assume that users (items) with similar representations exist in the same latent space. By mapping all entities representations into a collaborative semantic space, we are able to capture the underlying "commonality" features among entities. Then, we include them into contrastive pairs, to better mine users (items) features and relationships. Extensive experiments on three real-world public datasets show that our KGSC consistently outperforms state-of-the-art techniques.