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Found 12 papers, 6 papers with code
ARMOR v0.1: Empowering Autoregressive Multimodal Understanding Model with Interleaved Multimodal Generation via Asymmetric Synergy
Existing UniMs are designed to simultaneously learn both multimodal understanding and generation capabilities, demanding substantial computational resources, and often struggle to generate interleaved text-image.
Modulating CNN Features with Pre-Trained ViT Representations for Open-Vocabulary Object Detection
On OV-COCO, the proposed method achieves 44. 3 AP$_{50}^{\mathrm{novel}}$ with ViT-B/16 and 48. 5 AP$_{50}^{\mathrm{novel}}$ with ViT-L/14.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning
We first construct a Vision Question Answering (VQA) dataset of 63. 8k high-quality examples with our propose Referent Method, which ensures the model's responses are highly depend on visual content within the image.
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
So we propose SNRO, which slightly shifts the features of new classes to remember old classes.
Class Incremental Learning with Multi-Teacher Distillation
In this paper we propose the MTD method to find multiple diverse teachers for CIL.
A simple thinking about the application of the attention mechanism in medical ultrasound image segmentation task
The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images.
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation
Breast cancer is one of the common cancers that endanger the health of women globally.
Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation
However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors.
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation
Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention.
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Different from existing attention mechanisms, the hybrid adaptive attention module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation.
