Search Results for author:
Found 12 papers, 6 papers with code
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression.
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything.
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
We identify a critical bias in contemporary CLIP-based models, which we denote as \textit{single tag bias}.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on COCO-Stuff-171
(mIoU metric)
Multi-Label Text Classification
Open Vocabulary Semantic Segmentation
+3
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis
This study demonstrates the first in-hospital adaptation of a cloud-based AI, similar to ChatGPT, into a secure model for analyzing radiology reports, prioritizing patient data privacy.
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus
The cone-beam computed tomography (CBCT) provides 3D volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease.
Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio
The Single-Instrument Encoder is trained to classify the instruments used in single-track audio, and we take its penultimate layer's activation as the instrument embedding.
Exploring Train and Test-Time Augmentations for Audio-Language Learning
In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance.
Ranked #2 on
Audio to Text Retrieval
on AudioCaps
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
Explaining generalizations and preventing over-confident predictions are central goals of studies on the loss landscape of neural networks.
Improved Chest Anomaly Localization without Pixel-level Annotation via Image Translation Network Application in Pseudo-paired Registration Domain
For the first stage, we introduce an advanced deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps, by sequentially utilizing linear-based global and uniform coordinate transformation and AI-based non-linear coordinate fine-tuning.
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks
Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application.
Ranked #10 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Collaborative Method for Incremental Learning on Classification and Generation
In this paper, we introduce a novel algorithm, Incremental Class Learning with Attribute Sharing (ICLAS), for incremental class learning with deep neural networks.
