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Found 22 papers, 2 papers with code
SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning
With only 3k training samples, SAM-R1 achieves strong performance across multiple benchmarks, demonstrating the effectiveness of reinforcement learning in equipping multimodal models with segmentation-oriented reasoning capabilities.
RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations
Existing KV quantization rely on fine-grained quantization or the retention of a significant portion of high bit-widths caches, both of which compromise compression ratio and often fail to maintain robustness at extremely low average bit-widths.
AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models
Vision-language models (VLMs) show remarkable performance in multimodal tasks.
Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation
While some pioneering studies have undertaken preliminary explorations, they still remain at the level of aligned encoders (e. g., CLIP) and lack exploration of misaligned encoders.
Ranked #1 on
Referring Expression Segmentation
on RefCOCO
Sharingan: Extract User Action Sequence from Desktop Recordings
Video recordings of user activities, particularly desktop recordings, offer a rich source of data for understanding user behaviors and automating processes.
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification
This paper focuses on the classification task of breast ultrasound images and researches on the reliability measurement of classification results.
BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification
Artificial intelligence(AI)-assisted method had received much attention in the risk field such as disease diagnosis.
MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement
In this paper, inspired by self-training of semi-supervised learning, we pro? pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR).
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
The medical datasets are usually faced with the problem of scarcity and data imbalance.
A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts
Fetal standard scan plane detection during 2-D mid-pregnancy examinations is a highly complex task, which requires extensive medical knowledge and years of training.
AI assisted method for efficiently generating breast ultrasound screening reports
Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing.
Knowledge AI: New Medical AI Solution for Medical image Diagnosis
The implementation of medical AI has always been a problem.
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination
Significance: 1) The proposed method makes the target detection model more suitable for diagnosing breast ultrasound images.
Deformable Registration Using Average Geometric Transformations for Brain MR Images
Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis.
Facial Expression Recognition Research Based on Deep Learning
The greater the distance, the more sensitive the feature map is to the facial feature unit.
Medical Image Super-Resolution Using a Generative Adversarial Network
2) The strategy of similarity measurement is included three parts(patients' chief complaint, pathology results and medical images).
Efforts estimation of doctors annotating medical image
In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image.
The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features
In this paper, we use the differential geometric information including JD and CV as image characteristics to measure the differences between different MRI images, which represent local size changes and local rotations of the brain image, and we can use them as one CNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) to get more accurate results of brain segmentation.
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation
It is on the basis of this, we propose a judgement to distinguish data sets that different models are good at.
A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net
How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time.
