Search Results for author:
Found 14 papers, 6 papers with code
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language Models
In this paper, we propose fine-grained detoxification via instance-level prefixes (FGDILP) to mitigate toxic text without additional cost.
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze.
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors.
LGA-RCNN: Loss-Guided Attention for Object Detection
Object detection is widely studied in computer vision filed.
Self-Paced Uncertainty Estimation for One-shot Person Re-Identification
The one-shot Person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from $X$ to $Y$.
Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation
To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning.
AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection
In this paper, we carefully analyze the characteristics of FSOD, and present that a general few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations.
Automatic classification of multiple catheters in neonatal radiographs with deep learning
Performance was similar for the set of 58 test images consisting of 2 or more catheters, with an AP of 0. 975 (0. 255-1. 000) for NGTs, 0. 997 (0. 009-1. 000) for ETTs, 0. 981 (0. 797-0. 998) for UACs, and 0. 937 (0. 689-0. 990) for UVCs.
Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?
Catheters are the second most common abnormal finding on radiographs.
Focus-Enhanced Scene Text Recognition with Deformable Convolutions
Recently, scene text recognition methods based on deep learning have sprung up in computer vision area.
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
In this work, we proposed a simple way of synthesizing catheters on X-ray images and a scale recurrent network for catheter detection.
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Melanoma is a curable aggressive skin cancer if detected early.
Sharpness-aware Low dose CT denoising using conditional generative adversarial network
Low Dose Computed Tomography (LDCT) has offered tremendous benefits in radiation restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance.
