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Found 14 papers, 5 papers with code
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Finally, SAM is prompted by the retrieved ROI to segment a specific organ.
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA.
Biased Attention: Do Vision Transformers Amplify Gender Bias More than Convolutional Neural Networks?
We examine bias amplification when models belonging to these two architectures are used as a part of large multimodal models, evaluating the different image encoders of Contrastive Language Image Pretraining which is an important model used in many generative models such as DALL-E and Stable Diffusion.
An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans
Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality.
Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models
In this paper, we propose Multimodal Composite Association Score (MCAS) as a new method of measuring gender bias in multimodal generative models.
Constructing a meta-learner for unsupervised anomaly detection
Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools.
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks.
TrollsWithOpinion: A Dataset for Predicting Domain-specific Opinion Manipulation in Troll Memes
To enable this analysis, we enhanced an existing dataset by annotating the data with our defined classes, resulting in a dataset of 8, 881 IWT or multimodal memes in the English language (TrollsWithOpinion dataset).
Utilising Visual Attention Cues for Vehicle Detection and Tracking
We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.
ECIR 2020 Workshops: Assessing the Impact of Going Online
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation
This work tackles the Pixel Privacy task put forth by MediaEval 2019.
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function.
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification.
Fully Convolutional Crowd Counting On Highly Congested Scenes
In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016).
