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Found 14 papers, 4 papers with code
Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks.
Nash Meets Wertheimer: Using Good Continuation in Jigsaw Puzzles
In an attempt to fill in this gap, in this paper we introduce a new challenging version of the puzzle solving problem in which one deliberately ignores conventional color and shape features and relies solely on the presence of linear geometrical patterns.
Reassembling Broken Objects using Breaking Curves
Results show that our solution performs well in reassembling different kinds of broken objects.
Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans
What is the creative process through which an artist goes from an original image to a painting?
Transductive Visual Verb Sense Disambiguation
Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence.
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets
The availability of large-scale data sets is an essential pre-requisite for deep learning based semantic segmentation schemes.
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
We propose a multimodal approach for detection of students' behavioral engagement states (i. e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse.
Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study.
The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students
One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.
Compressively Sensed Image Recognition
Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal.
Ancient Coin Classification Using Graph Transduction Games
Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins.
