179 research outputs found
FABLE : Fabric Anomaly Detection Automation Process
Unsupervised anomaly in industry has been a concerning topic and a stepping
stone for high performance industrial automation process. The vast majority of
industry-oriented methods focus on learning from good samples to detect anomaly
notwithstanding some specific industrial scenario requiring even less specific
training and therefore a generalization for anomaly detection. The obvious use
case is the fabric anomaly detection, where we have to deal with a really wide
range of colors and types of textile and a stoppage of the production line for
training could not be considered. In this paper, we propose an automation
process for industrial fabric texture defect detection with a
specificity-learning process during the domain-generalized anomaly detection.
Combining the ability to generalize and the learning process offer a fast and
precise anomaly detection and segmentation. The main contributions of this
paper are the following: A domain-generalization texture anomaly detection
method achieving the state-of-the-art performances, a fast specific training on
good samples extracted by the proposed method, a self-evaluation method based
on custom defect creation and an automatic detection of already seen fabric to
prevent re-training.Comment: 7th International Conference on Control, Automation and Diagnosis
(ICCAD'23), 6 page
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
The privacy protection mechanism of federated learning (FL) offers an
effective solution for cross-center medical collaboration and data sharing. In
multi-site medical image segmentation, each medical site serves as a client of
FL, and its data naturally forms a domain. FL supplies the possibility to
improve the performance of seen domains model. However, there is a problem of
domain generalization (DG) in the actual de-ployment, that is, the performance
of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is
proposed to solve the DG of FL in this study. Specifically, the model-level
attention module (MLA) and batch-instance style normalization (BIN) block were
designed. The MLA represents the unseen domain as a linear combination of seen
domain models. The atten-tion mechanism is introduced for the weighting
coefficient to obtain the optimal coefficient ac-cording to the similarity of
inter-domain data features. MLA enables the global model to gen-eralize to
unseen domain. In the BIN block, batch normalization (BN) and instance
normalization (IN) are combined to perform the shallow layers of the
segmentation network for style normali-zation, solving the influence of
inter-domain image style differences on DG. The extensive experimental results
of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN
outperforms state-of-the-art methods.Comment: 9 pages, 8 figures, 2 table
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