Entity grounding, a crucial task in constructing multimodal knowledge graphs, aims to align entities from knowledge graphs with their corresponding images.Unlike conventional visual grounding tasks that use referring expressions (REs) as inputs, entity grounding relies solely on entity names and types, presenting a significant challenge.To address this, we introduce a novel Iterative Self-Refinement (ISR) scheme to enhance the multimodal large language model's capability to generate high quality REs for the given entities as explicit contextual clues.This training scheme, inspired by human learning dynamics and human annotation processes, enables the MLLM to iteratively generate and refine REs by learning from successes and failures, guided by outcome rewards from a visual grounding model.This iterative cycle of self-refinement avoids overfitting to fixed annotations and fosters continued improvement in referring expression generation.Extensive experiments demonstrate that our methods surpasses other methods in entity grounding, highlighting its effectiveness, robustness and potential for broader applications 1 .

Detailed descriptions of human motion are crucial for effective fitness training, which highlights the importance of research in fine-grained human motion video captioning. Existing video captioning models often fail to capture the nuanced semantics of videos, resulting in the generated descriptions that are coarse and lack details, especially when depicting human motions. To benchmark the Body Fitness Training scenario, in this paper, we construct a fine-grained human motion video captioning dataset named BoFiT and design a state-of-the-art baseline model named BoFiT-Gen (Body Fitness Training Text Generation). BoFiT-Gen makes use of computer vision techniques to extract angular representations of human motions from videos and LLMs to generate fine-grained descriptions of human motions via prompting. Results show that BoFiT-Gen outperforms previous methods on comprehensive metrics. We aim for this dataset to serve as a useful evaluation set for visio-linguistic models and drive further progress in this field. Our dataset is released at https://github.com/colmon46/bofit.

With the rapid development of Internet of Things, the amount and distribution of malware has greatly increased. Internet of Things platform needs new defense technologies to protect users from new the increasing number and complexity of malware. This article extracts import Dlls and import APIs from the original portable executable (PE) file, and uses heterogeneous graph to describe higher-level semantic relationship between two PE files. Besides this we construct four static features to comprehensively describe PE file. Based on ensemble learning we develop a model called robust ensemble model based on semantic feature fusion (REMSF) which fuses five features mentioned above. To evaluate REMSF, we collect 5370 executable PE files from the real world for series of experiments, in which REMSF’s detection accuracy can reach 99.07%.