Compared with low-bit models trained directly, the proposed framework brings 0. 5% to 3. 4% accuracy gains to three different quantization schemes.
Referring Image Segmentation (RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks.
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains.
Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares.
NFs, temporally adjacent to the labeled frame, often contain rich motion information that assists in the accurate localization of sounding objects.
To tackle these challenges, this paper introduces a novel graph construction method tailored to free-floating traffic mode.
In this paper, we aim to develop an open-source, multilingual language model for medicine, that the benefits a wider, linguistically diverse audience from different regions.
Structured data offers a sophisticated mechanism for the organization of information.
In this paper, we explore a learning-based automatic bone quality classification method for spinal metastasis based on CT images.
In this paper, we propose a Weakly supervised Iterative Spinal Segmentation (WISS) method leveraging only four corner landmark weak labels on a single sagittal slice to achieve automatic volumetric segmentation from CT images for VBs.