Determinación de actitud

Learning good fine-grained visual representations is very important for fine-grained recognition, image generation and semantic segmentation tasks. Based on our pro- posed methods, there are many possible improvements or extensions. Some of them are discussed below.

Although Generative Adversarial Networks (GANs) have shown remarkable suc- cess in various tasks, they still face challenges in generating high quality images. In this thesis, our AttnGAN has significantly improved the performance of GAN models for text-to-image generation because of the proposed attention mechanisms. How- ever, the AttnGAN still struggles on generating photo-realistic images on multi-class datasets (e.g.COCO). By carefully examining the generated samples, we observe that

it is very difficult for the AttnGAN to capture geometric or structural patterns that occur consistently in some objects (e.g., animals). As discussed by Zhanget al. [124], one possible explanation for this is that heavily relying on convolution prevents GAN models learning about long-term dependencies. To tackle this challenge, they in- troduced a self-attention mechanism into convolutional GANs. The self-attention module has shown strong ability to model long range, multi-level dependencies across image regions. Different from our fine-grained attention (i.e., the attention over word embeddings within an input sequence) discussed in Chapter4, the self-attention pro- posed in [124] is the attention over internal model states. In the future, we can explore the integration of our fine-grained attention mechanism and the self-attention mechanism for text-to-image generation on multi-class datasets.

Moreover, as the first GAN-inspired framework adapted specifically for the seg- mentation task, our proposed SegAN has produced superior segmentation accuracy. However, from the comparison results, we also observe that the SegAN model still has some drawbacks when segmenting the core and Gd-enhanced regions. While the SegAN can extract different levels of features, segmentation for relatively small re- gions such as core and Gd-enhanced may need more focus on pixel-level features. Thus, previous methods using pixel-level loss could have better performance than the proposed SegAN for segmenting these small regions under some circumstances. One possible improvement for future work can be using different network architectures for segmenting different types of regions. Another drawback that we observe is that, although our model can be easily extended to semantic segmentation tasks that have many label classes, the computational cost can be quite high when the number of classes is large. For instance, in a task with m different classes, to achieve best per-

formance, we can buildmS1-1C models (i.e., one segmentor and one critic per class) to generate segmentation masks for the m classes. However, a major limitation is that such a model would have high computational cost whenm is very large. In the future work, we can investigate variants of the SegAN architecture in order to reduce computational cost without sacrificing accuracy.

In addition, it is a promising direction to build the bridge between fine-grained image recognition and generation with Generative Adversarial Networks. Large num- ber of categories and the lack of training data are main factors that make fine-grained categorization more challenging. Thus, one intuitive bridge is to apply GANs to pro- duce more training images for fine-grained classification. Another option is to utilize GANs to generate object parts with different poses and viewpoints.

Last but not the least, as general frameworks, our multimodal deep neural net- work in Chapter3and SegAN in Chapter5in Chapter are not limited to the medical domain. We can investigate their potential for general image classification and seg- mentation tasks in the future.

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