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Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
[article]
2022
arXiv
pre-print
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Our goal is to leverage ViT's patch tokens and self-attention mechanism to mine rich instances in multi-label images, named diverse instance discovery (DiD). ...
Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. ...
To this end, we propose Diverse instance discovery: Vision-Transformer for instance-aware Multi-label Image Recognition. ...
arXiv:2204.10731v1
fatcat:a6xikibuyjg7bl2vikbogaakbm
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
[article]
2018
arXiv
pre-print
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Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. ...
Our model discovers geometrically and semantically consistent keypoints across viewing angles and instances of an object category. ...
These keypoints will serve as a building block for feature representations based on a sparse set of points, useful for geometric reasoning and pose-aware or pose-invariant object recognition (e.g., [43 ...
arXiv:1807.03146v2
fatcat:zmmvnpamx5a57dfz2a7bgtqhkm
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
[article]
2021
arXiv
pre-print
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each frame, enabling more accurate object discovery. ...
This paper addresses the task of unsupervised video multi-object segmentation. ...
In recent years, image-level instance segmentation has attracted great research interests, which extends semantic segmentation [30, 28, 52, 61] to assign different labels for separate instances of objects ...
arXiv:2104.04782v1
fatcat:7qa2244w7rgl5ez26euybjcrlq
Automatic Discovery of Political Meme Genres with Diverse Appearances
[article]
2020
arXiv
pre-print
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In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. ...
Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content ...
Figure 14 : A crossover between the dyed finger meme and a meme that shows a number of fingers equal to the spot on the ballot of the candidate for which one was voting. ...
arXiv:2001.06122v2
fatcat:sb3w55ovn5dvjaerhqbh3svxcu
Structure Diversity-induced Anchor Graph Fusion for Multi-view Clustering
2022
ACM Transactions on Knowledge Discovery from Data
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To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. ...
The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. ...
vision [2] , pattern recognition [4] and data minimization [40] . ...
doi:10.1145/3534931
fatcat:25aztaztnzbc7e23yhul3k36sa
Drug discovery with explainable artificial intelligence
2020
Nature Machine Intelligence
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V arious concepts of 'artificial intelligence' (AI) have been successfully adopted for computer-assisted drug discovery in the past few years 1-3 . ...
Given the current pace of AI in drug discovery and related fields, there will be an increased demand for methods that help us understand and interpret the underlying models. ...
This methodology has successfully been applied in several tasks such as image recognition, text classification and visual question answering 82 . • Counterfactual instance search. ...
doi:10.1038/s42256-020-00236-4
fatcat:nlkwpc2jvvhcblmiulbdzzxaiq
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In this work, we propose a geometry-aware 3D representation for the human pose to address this limitation by using multiple views in a simple auto-encoder model at the training stage and only 2D keypoint ...
Recent studies have shown remarkable advances in 3D human pose estimation from monocular images, with the help of large-scale in-door 3D datasets and sophisticated network architectures. ...
This task is an active research topic in the computer vision community for serving as a key step for many applications, e.g., action recognition, human-computer interaction, and autonomous * Xipeng Chen ...
doi:10.1109/cvpr.2019.01115
dblp:conf/cvpr/ChenLLQL19
fatcat:jmcyrerwbnayvawaaer7zsl6qq
Unsupervised Learning from Videos for Object Discovery in Single Images
2020
Symmetry
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We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as ...
object instances from a single image. ...
, recognition, instance segmentation, etc. ...
doi:10.3390/sym13010038
fatcat:ohgmg6gmp5culcppemdmhvv42y
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
[article]
2022
arXiv
pre-print
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Our new NovelCraft dataset contains multi-modal episodic data of the images and symbolic world-states seen by an agent completing a pogo-stick assembly task within a video game world. ...
Results suggest an opportunity for future research: models aware of task-specific costs of different types of mistakes could more effectively detect and adapt to novelty in open worlds. ...
This approach continues to dominate vision research related to novelty detection: The Open World Vision workshop at CVPR 2021 used repurposed images from ImageNet in their open-set recognition challenge ...
arXiv:2206.11736v1
fatcat:drhaq6bvcnahlizbpjyw7liuru
Visual Emotion Analysis via Affective Semantic Concept Discovery
2022
Scientific Programming
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image emotion recognition. ...
Capturing the emotions embedded in these social images has always been important yet challenging. ...
Nevertheless, because of the complexity and diversity of image emotion recognition, a large amount of labelled training data with small noise is required to achieve good performance. ...
doi:10.1155/2022/6975490
doaj:22d1411f2825471e957e0db028a95c2d
fatcat:v2jnxauo2feglien77updwte7e
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation
[article]
2019
arXiv
pre-print
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In this work, we propose a geometry-aware 3D representation for the human pose to address this limitation by using multiple views in a simple auto-encoder model at the training stage and only 2D keypoint ...
Recent studies have shown remarkable advances in 3D human pose estimation from monocular images, with the help of large-scale in-door 3D datasets and sophisticated network architectures. ...
[43] discovery landmark structure as an intermediate representation for image autoencoding with several constraints. ...
arXiv:1903.08839v2
fatcat:mgy72vpyfjepjilklnnijvmpqa
Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
[article]
2022
arXiv
pre-print
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This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics. ...
In this chapter we aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML. ...
Recognition for Non-images by Kovalerchuk, Kalla, and Agarwal. ...
arXiv:2205.01296v2
fatcat:s3ygky7tcrg2jaj4ihccyg2lzy
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
[article]
2020
arXiv
pre-print
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TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. ...
Her research interests are computer vision and pattern recognition. ...
Her research interests include pattern recognition, computer vision and target tracking. ...
arXiv:1902.09968v3
fatcat:2col2budbjgzjoykscdl632qv4
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
2017
IEEE Transactions on Knowledge and Data Engineering
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2016 pre-print arXiv:1612.08544v1 fatcat:mckdopewkvaovcxpyx5psgnhjmOther Versions
The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. ...
Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery ...
It is possible to build predictive models that use multi-spectral data from satellite images as input features to classify pixels of the image as water or land. ...
doi:10.1109/tkde.2017.2720168
fatcat:ijqhkol5hrf3fnxjhthmbce7he
Applications of machine learning in drug discovery and development
2019
Nature reviews. Drug discovery
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Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. ...
Drug discovery and development pipelines are long, complex and depend on numerous factors. ...
Papa for helpful comments, M. Segler for contributing to the small-molecule optimization subsection and A. Janowczyk for providing the pathology images in Figure 4 . ...
doi:10.1038/s41573-019-0024-5
pmid:30976107
pmcid:PMC6552674
fatcat:4gubtr5kz5fe7khrbtfrre4lhy
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