36 research outputs found
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble
of multiple student models collaboratively and distill knowledge from each
other. Although existing online KD methods achieve desirable performance, they
often focus on class probabilities as the core knowledge type, ignoring the
valuable feature representational information. We present a Mutual Contrastive
Learning (MCL) framework for online KD. The core idea of MCL is to perform
mutual interaction and transfer of contrastive distributions among a cohort of
networks in an online manner. Our MCL can aggregate cross-network embedding
information and maximize the lower bound to the mutual information between two
networks. This enables each network to learn extra contrastive knowledge from
others, leading to better feature representations, thus improving the
performance of visual recognition tasks. Beyond the final layer, we extend MCL
to intermediate layers and perform an adaptive layer-matching mechanism trained
by meta-optimization. Experiments on image classification and transfer learning
to visual recognition tasks show that layer-wise MCL can lead to consistent
performance gains against state-of-the-art online KD approaches. The
superiority demonstrates that layer-wise MCL can guide the network to generate
better feature representations. Our code is publicly avaliable at
https://github.com/winycg/L-MCL.Comment: 18 pages, accepted by IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI-2023
Numerical Study on Elastic Parameter Identification of Large-Span Steel Truss Structures Based on Strain Test Data
Large-span steel trusses are widely used in public buildings such as large-span factory buildings, exhibition halls, gymnasiums, and bridges because of their fast construction speed and easy industrial manufacturing. Due to construction errors and environmental factors, the material properties may change during their service life, and it is an important prerequisite for the structural safety assessment to identify the true material parameters of the structure. Among the many parameters, the elastic modulus is one that has the greatest impact on the accuracy of structural safety analysis. In this paper, a mathematical analysis model of elastic modulus identification was constructed, based on the strain test data and the improved gradient regularization method. The relationship between the strain test data and elastic moduli was established. A common finite element program based on the method was developed to identify the elastic modulus. A series of numerical simulations was carried out on a 53-element steel truss model to study the availability and numerical stability of the method. The effects of different initial values, numbers of strain tests, and locations of the strain test as well as the number of unknown parameters on the identification results were studied. The results showed that the proposed method had very high accuracy and computational efficiency. For the case of 53 unknown parameters without considering the test error, the identification accuracy could reach a 1 × 10−10 order of magnitude after only several iterations. This paper provides an effective solution to obtain the actual values of the elastic modulus of steel truss structures in practical engineering
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense
rasterized scene representation (e.g., agent occupancy and semantic map) to
perform planning, which is computationally intensive and misses the
instance-level structure information. In this paper, we propose VAD, an
end-to-end vectorized paradigm for autonomous driving, which models the driving
scene as a fully vectorized representation. The proposed vectorized paradigm
has two significant advantages. On one hand, VAD exploits the vectorized agent
motion and map elements as explicit instance-level planning constraints which
effectively improves planning safety. On the other hand, VAD runs much faster
than previous end-to-end planning methods by getting rid of
computation-intensive rasterized representation and hand-designed
post-processing steps. VAD achieves state-of-the-art end-to-end planning
performance on the nuScenes dataset, outperforming the previous best method by
a large margin. Our base model, VAD-Base, greatly reduces the average collision
rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny,
greatly improves the inference speed (up to 9.3x) while achieving comparable
planning performance. We believe the excellent performance and the high
efficiency of VAD are critical for the real-world deployment of an autonomous
driving system. Code and models will be released for facilitating future
research.Comment: Code&Demos: https://github.com/hustvl/VA
The asparagus genome sheds light on the origin and evolution of a young Y chromosome
Several models have been proposed to explain the emergence of sex chromosomes. Here, through comparative genomics and mutant analysis, Harkess et al. show that linked but separate genes on the Y chromosome are responsible for sex determination in Asparagus, supporting a two-gene model for sex chromosome evolution
Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2
Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi