10,284 research outputs found
Constructing Balance from Imbalance for Long-tailed Image Recognition
Long-tailed image recognition presents massive challenges to deep learning
systems since the imbalance between majority (head) classes and minority (tail)
classes severely skews the data-driven deep neural networks. Previous methods
tackle with data imbalance from the viewpoints of data distribution, feature
space, and model design, etc.In this work, instead of directly learning a
recognition model, we suggest confronting the bottleneck of head-to-tail bias
before classifier learning, from the previously omitted perspective of
balancing label space. To alleviate the head-to-tail bias, we propose a concise
paradigm by progressively adjusting label space and dividing the head classes
and tail classes, dynamically constructing balance from imbalance to facilitate
the classification. With flexible data filtering and label space mapping, we
can easily embed our approach to most classification models, especially the
decoupled training methods. Besides, we find the separability of head-tail
classes varies among different features with different inductive biases. Hence,
our proposed model also provides a feature evaluation method and paves the way
for long-tailed feature learning. Extensive experiments show that our method
can boost the performance of state-of-the-arts of different types on
widely-used benchmarks. Code is available at https://github.com/silicx/DLSA.Comment: Accepted to ECCV 202
Learning to Anticipate Future with Dynamic Context Removal
Anticipating future events is an essential feature for intelligent systems
and embodied AI. However, compared to the traditional recognition task, the
uncertainty of future and reasoning ability requirement make the anticipation
task very challenging and far beyond solved. In this filed, previous methods
usually care more about the model architecture design or but few attention has
been put on how to train an anticipation model with a proper learning policy.
To this end, in this work, we propose a novel training scheme called Dynamic
Context Removal (DCR), which dynamically schedules the visibility of observed
future in the learning procedure. It follows the human-like curriculum learning
process, i.e., gradually removing the event context to increase the
anticipation difficulty till satisfying the final anticipation target. Our
learning scheme is plug-and-play and easy to integrate any reasoning model
including transformer and LSTM, with advantages in both effectiveness and
efficiency. In extensive experiments, the proposed method achieves
state-of-the-art on four widely-used benchmarks. Our code and models are
publicly released at https://github.com/AllenXuuu/DCR.Comment: CVPR 202
Prediction of NOx Emissions from a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques
This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results
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Linking Aboveground Traits to Root Traits and Local Environment: Implications of the Plant Economics Spectrum.
The plant economics spectrum proposes that ecological traits are functionally coordinated and adapt along environmental gradients. However, empirical evidence is mixed about whether aboveground and root traits are consistently linked and which environmental factors drive functional responses. Here we measure the strength of relationships between aboveground and root traits, and examine whether community-weighted mean trait values are adapted along gradients of light and soil fertility, based on the seedling censuses of 57 species in a subtropical forest. We found that aboveground traits were good predictors of root traits; specific leaf area, dry matter, nitrogen and phosphorus content were strongly correlated with root tissue density and specific root length. Traits showed patterns of adaptation along the gradients of soil fertility and light; species with fast resource-acquisitive strategies were more strongly associated with high soil phosphorus, potassium, openness, and with low nitrogen, organic matter conditions. This demonstrates the potential to estimate belowground traits from known aboveground traits in seedling communities, and suggests that soil fertility is one of the main factors driving functional responses. Our results extend our understanding of how ecological strategies shape potential responses of plant communities to environmental change
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