Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSemi-supervised estimation of event temporal length for cell event detection
321
0
0.0
(
0
)
Added by
Ha Tran Hong Phan
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of cell events for their ability to capture more discriminative features of cellular processes compared to traditional methods. In particular, convolutional long short-term memory (LSTM) models, which exploits the changes in cell events observable in video sequences, is the state-of-the-art for mitosis detection in cell videos. However, their limitations are the determination of the input sequence length, which is often performed empirically, and the need for a large annotated training dataset which is expensive to prepare. We propose a novel semi-supervised method of optimal length detection for mitosis detection with two key contributions: (i) an unsupervised step for learning the spatial and temporal locations of cells in their normal stage and approximating the distribution of temporal lengths of cell events and, (ii) a step of inferring, from that distribution, an optimal input sequence length and a minimal number of annotated frames for training a LSTM model for each particular video. We evaluated our method in detecting mitosis in densely packed stem cells in a phase-contrast microscopy videos. Our experimental data prove that increasing the input sequence length of LSTM can lead to a decrease in performance. Our results also show that by approximating the optimal input sequence length of the tested video, a model trained with only 18 annotated frames achieved F1-scores of 0.880-0.907, which are 10% higher than those of other published methods with a full set of 110 training annotated frames.
rate research
Read More
We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data. The guidance is guaranteed by the audio tagging performance gap of the two models. In the meantime, the student model liberated from the trade-off is able to provide more excellent boundary detection results. We propose a principle to design such two models based on the relation between the temporal compression scale and the two sub-targets. We also propose an end-to-end semi-supervised learning process for these two models to enable their abilities to rise alternately. Experiments on the DCASE2018 Task4 dataset show that our approach achieves competitive performance.
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. Labels for acoustic events are expensive to obtain, and relevant acoustic event audios can be limited, especially for rare events. In this paper we leverage an Internet-scale unlabeled dataset with potential domain shift to improve the detection of acoustic events. Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semisupervised self-training set-up, in all pre-defined acoustic event detection tasks. As our approach relies on ensemble models, we further show the improvements can be distilled to a single model via knowledge distillation, with the resulting single student model maintaining high accuracy of teacher ensemble models.
Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. However, existing deep learning methods did not address the importance of temporal information well from the perspective of architecture design and cannot effectively extract spatio-temporal features. Another line of research that utilizes Spiking Neural Network suffers from training issues for deeper architecture. To address these points, a novel input representation is proposed that captures the events temporal distribution for signal enhancement. Moreover, we introduce a spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images. Besides, our architecture allows some traditional frame-based core modules, such as correlation layer and iterative residual refine scheme, to be incorporated. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge Distillation (KD) has been widely used for semi-supervised image classification. However, adapting these methods for SSOD has the following obstacles. (1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student. (2) The class imbalance issue in SSOD hinders an efficient knowledge transfer from teacher to student. To address these problems, we propose a novel method Temporal Self-Ensembling Teacher (TSE-T) for SSOD. Differently from previous KD based methods, we devise a temporally evolved teacher model. First, our teacher model ensembles its temporal predictions for unlabeled images under stochastic perturbations. Second, our teacher model ensembles its temporal model weights with the student model weights by an exponential moving average (EMA) which allows the teacher gradually learn from the student. These self-ensembling strategies increase data and model diversity, thus improving teacher predictions on unlabeled images. Finally, we use focal loss to formulate consistency regularization term to handle the data imbalance problem, which is a more efficient manner to utilize the useful information from unlabeled images than a simple hard-thresholding method which solely preserves confident predictions. Evaluated on the widely used VOC and COCO benchmarks, the mAP of our method has achieved 80.73% and 40.52% on the VOC2007 test set and the COCO2014 minval5k set respectively, which outperforms a strong fully-supervised detector by 2.37% and 1.49%. Furthermore, our method sets the new state-of-the-art in SSOD on VOC2007 test set which outperforms the baseline SSOD method by 1.44%. The source code of this work is publicly available at http://github.com/syangdong/tse-t.
In recent years, the involvement of synthetic strongly labeled data,weakly labeled data and unlabeled data has drawn much research attentionin semi-supervised sound event detection (SSED). Self-training models carry out predictions without strong annotations and then take predictions with high probabilities as pseudo-labels for retraining. Such models have shown its effectiveness in SSED. However, probabilities are poorly calibrated confidence estimates, and samples with low probabilities are ignored. Hence, we introduce a method of learning confidence deliberately and retaining all data distinctly by applying confidence as weights. Additionally, linear pooling has been considered as a state-of-the-art aggregation function for SSED with weak labeling. In this paper, we propose a power pooling function whose coefficient can be trained automatically to achieve nonlinearity. A confidencebased semi-supervised sound event detection (C-SSED) framework is designed to combine confidence and power pooling. The experimental results demonstrate that confidence is proportional to the accuracy of the predictions. The power pooling function outperforms linear pooling at both error rate and F1 results. In addition, the C-SSED framework achieves a relative error rate reduction of 34% in contrast to the baseline model.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
