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Learning to recognize novel visual classes from few samples is challenging but promising. Previous studies have shown that few-shot model tends to overfit and lead to poor generalization performance, which is because it finds a biased distribution based on a few samples. In addition, in agriculture-specific domains, there are more serious research challenges such as imbalanced disease distribution, one-shot representation biases, fine-grained recognition, and granularity shift. As far as we know, this study is the first work on the fine-grained "Coarse-to-Fine" few-shot plant disease classification, which classifies "fine-grained novel classes" (specific to disease severity) based on "coarse-grained base classes" (specific to plant species). A complete two-stage in-use calibration strategy is presented in this paper. Firstly, we propose an attention-based inverse Mahalanobis distance weighted prototype calibration module (AIPCM). By transferring statistics from sample-rich coarse-grained base classes to sample-scarce fine-grained novel classes, we achieve prototype calibration for 1-shot sample and obtain an unbiased distribution in the feature space. Secondly, to generate more reasonable decision boundaries, we propose a prior-driven task-adapted decision boundary calibration module (TDBCM) based on class-covariance metric. The original Euclidean/Cosine distance is updated to the Mahalanobis distance by introducing the prior mean and covariance of the high-dimensional features. Experimental results on several datasets demonstrate that our model outperforms the state-of-the-art (SOTA) models. It can be said that our work is a valuable supplement to the domain-specific agricultural applications. [ABSTRACT FROM AUTHOR]

In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. [ABSTRACT FROM AUTHOR]

Recovering the 3D shape of an object from single or multiple images with deep neural networks has been attracting increasing attention in the past few years. Mainstream works (e.g. 3D-R2N2) use recurrent neural networks (RNNs) to sequentially fuse feature maps of input images. However, RNN-based approaches are unable to produce consistent reconstruction results when given the same input images with different orders. Moreover, RNNs may forget important features from early input images due to long-term memory loss. To address these issues, we propose a novel framework for single-view and multi-view 3D object reconstruction, named Pix2Vox++. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume. To further correct the wrongly recovered parts in the fused 3D volume, a refiner is adopted to generate the final output. Experimental results on the ShapeNet, Pix3D, and Things3D benchmarks show that Pix2Vox++ performs favorably against state-of-the-art methods in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Since the ancient times, free-hand sketch has been widely used as an effective and convenient intermediate means to express human thoughts and highly diverse objects in reality. In recent years, a great quantity of researchers realized the significance of sketch and gradually focused on sketch-related problems, such as sketch-based image retrieval and recognition. Despite so many achievements, very few works concentrate on exploring the intrinsic factors which potentially influence the vivid degree of sketch. In this paper, we propose a weak supervised approach to discover the most discriminative patches for different categories of sketches, which perhaps grasp the key to a good free-hand sketch. In the beginning, we randomly extract tens of thousands of patches at multiple scales. After that, pyramid histogram of oriented gradient is calculated to represent these patches as an effective and uniform feature representation. To find the most discriminative patches for each class of sketches, we design an iterative detection process which combines cluster merging and discriminative ranking. The experimental results on the TU-Berlin sketch benchmark dataset demonstrate the effectiveness of the proposed method, as compared to other available approaches. Moreover, a reasonable analysis and discussion about good and bad sketches is provided based on the visual results. [ABSTRACT FROM AUTHOR]

Traditional particle filter which uses simple geometric shapes for representation cannot track objects with complex shape accurately. In this paper, we propose a refined particle filter method for contour tracking based on a determined binary level set model (DBLSM). In contrast with other previous work, the computational efficiency is greatly improved due to the simple form of the level set function. The DBLSM adds prior knowledge of the target model to the implementation of curve evolution which improves the curve acting principle and ensures a more accurate convergence to the target. Finally, we perform curve evolution in the update step of particle filter to make good use of the observation at current time. Some appearance information are considered together with the energy function to measure weights for particles, which can identify the target more accurately. Experiment results on several challenging video sequences have verified the proposed algorithm is efficient and effective in many complicated scenes. [ABSTRACT FROM AUTHOR]

Graph convolution networks (GCNs) based methods for 3D human pose estimation usually aggregate immediate features of single-hop nodes, which are unaware of the correlation of multi-hop nodes and therefore neglect long-range dependency for predicting complex poses. In addition, they typically operate either on single-scale or sequential down-sampled multi-scale graph representations, resulting in the loss of contextual information or spatial details. To address these problems, this paper proposes a parallel hop-aware graph attention network (PHGANet) for 3D human pose estimation, which learns enriched hop-aware correlation of the skeleton joints while maintaining the spatially-precise representations of the human graph. Specifically, we propose a hop-aware skeletal graph attention (HSGAT) module to capture the semantic correlation of multi-hop nodes, which first calculates skeleton-based 1-hop attention and then disseminates it to arbitrary hops via graph connectivity. To alleviate the redundant noise introduced by the interactions with distant nodes, HSGAT uses an attenuation strategy to separate attention from distinct hops and assign them learnable attenuation weights according to their distances adaptively. Upon HSGAT, we further build PHGANet with multiple parallel branches of stacked HSGAT modules to learn the enriched hop-aware correlation of human skeletal structures at different scales. In addition, a joint centrality encoding scheme is proposed to introduce node importance as a bias in the learned graph representation, which makes the core joints (e.g., neck and pelvis) more influential during node aggregation. Experimental results indicate that PHGANet performs favorably against state-of-the-art methods on the Human3.6M and MPI-INF-3DHP benchmarks. Models and code are available at https://github.com/ChenyangWang95/PHGANet/. [ABSTRACT FROM AUTHOR]