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Found 10 papers, 4 papers with code
Quantum Visual Feature Encoding Revisited
Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited.
QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
In addition, we present QClusformer, a variant based on the Transformer architecture, tailored for unsupervised vision clustering tasks.
BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning
Our experiments demonstrate that BRACTIVE effectively identifies person-specific regions of interest, such as face and body-selective areas, aligning with neuroscience findings and indicating potential applicability to various object categories.
Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI
This loss function mimics brain activity patterns from these regions in the deep neural network using fMRI data.
The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution
The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed.
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal.
Micron-BERT: BERT-based Facial Micro-Expression Recognition
By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks.
Ranked #1 on
Micro Expression Recognition
on SMIC
Micro Expression Recognition
Micro-Expression Recognition
+1
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition
This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism.
Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis
However, most of current methods for self-supervised learning try to understand only semantic features of the data, but have not fully utilized properties inherent in medical images.
