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Found 10 papers, 4 papers with code
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images
In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data.
CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion
In this paper, we propose Cross-View Synthesis Transformer (CVSformer), which consists of Multi-View Feature Synthesis and Cross-View Transformer for learning cross-view object relationships.
Spatio-Temporal Context Modeling for Road Obstacle Detection
The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection.
A Multi-Scale Framework for Out-of-Distribution Detection in Dermoscopic Images
The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments.
Fourier Contour Embedding for Arbitrary-Shaped Text Detection
One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances.
A Multi-Level Approach to Waste Object Segmentation
We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects.
Adaptive GNN for Image Analysis and Editing
In practical verification, we design a new regularization structure with guided feature to produce GNN-based filtering and propagation diffusion to tackle the ill-posed inverse problems of quotient image analysis (QIA), which recovers the reflectance ratio as a signature for image analysis or adjustment.
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms.
Ranked #2 on
Facial Beauty Prediction
on SCUT-FBP
SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception.
A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem.
