The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them. In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.

Open-vocabulary 3D object detection (OV-3Det) aims to generalize beyond the limited number ofbasecategories labeled during thetraining phase. Thebiggest bottleneck is the scarcity of annotated 3D data, whereas 2D image datasets are abundantandrichlyannotated.

A new framework is proposed in this paper to address these issues.

Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spectrum--typically containing

To address the error propagation problem, we identify image fusion as the key point for improvement.

We introduce CogVLM, a powerful open-source visual language foundation model.

MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method.