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Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.

Few-shot classification (FSC), which aims to identify novel classes in the presence of a few labeled samples, has drawn vast attention in recent years. One of the representative few-shot classification methods is model-agnostic meta-learning (MAML), which focuses on learning an initialization that can quickly adapt to novel categories with a few annotated samples. However, due to insufficient samples, MAML can easily fall into the dilemma of overfitting. Most existing MAML-based methods either improve the inner-loop update rule to achieve better generalization or constrain the outer-loop optimization to learn a more desirable initialization, without considering improving the two optimization processes jointly, resulting in unsatisfactory performance. In this paper, we propose a bi-level constrained MAML (BLC-MAML) method for few-shot classification. Specifically, in the inner-loop optimization, we introduce a supervised contrastive loss to constrain the adaptation procedure, which can effectively increase the intra-class aggregation and inter-class separability, thus improving the generalization of the adapted model. In the case of the outer loop, we propose a cross-task metric (CTM) loss to constrain the adapted model to perform well on the different few-shot task. The CTM loss can enforce the adapted model to learn more discriminative and generalized feature representations, further boosting the generalization of the learned initialization. By simultaneously constraining the bi-level optimization procedure, the proposed BLC-MAML can learn an initialization with better generalization. Extensive experiments on several FSC benchmarks show that our method can effectively improve the performance of MAML under both the within-domain and cross-domain settings, and also perform favorably against the state-of-the-art FSC algorithms.