Deep neural networks for semantic segmentation always require a large number of samples with pixel-level labels, which becomes the major difficulty in their real-world applications. To reduce the labeling cost, unsupervised domain adaptation (UDA) approaches are proposed to transfer knowledge from labeled synthesized datasets to unlabeled real-world datasets. Recently, some semi-supervised learning methods have been applied to UDA and achieved state-of-the-art performance. One of the most popular approaches in semi-supervised learning is the entropy minimization method. However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer. To balance the gradient of well-classified target samples, we propose the maximum squares loss. Our maximum squares loss prevents the training process being dominated by easy-to-transfer samples in the target domain. Besides, we introduce the image-wise weighting ratio to alleviate the class imbalance in the unlabeled target domain. Both synthetic-to-real and cross-city adaptation experiments demonstrate the effectiveness of our proposed approach. The code is released at https://github. com/ZJULearning/MaxSquareLoss.

Abstract-We consider the case of inpainting single depth images. Without corresponding color images, previous or next frames, depth image inpainting is quite challenging. One natural solution is to regard the image as a matrix and adopt the low rank regularization just as inpainting color images. However, the low rank assumption does not make full use of the properties of depth images .A shallow observation may inspire us to penalize the non-zero gradients by sparse gradient regularization. However, statistics show that though most pixels have zero gradients, there is still a non-ignorable part of pixels whose gradients are equal to 1. Based on this specific property of depth images , we propose a low gradient regularization method in which we reduce the penalty for gradient 1 while penalizing the non-zero gradients to allow for gradual depth changes. The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting. We compare our proposed low gradient regularization with sparse gradient regularization. The experimental results show the effectiveness of our proposed approach.

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Video question answering is an important task toward scene understanding and visual data retrieval. However, current visual question answering works mainly focus on a single static image, which is distinct from the dynamic and sequential visual data in the real world. Their approaches cannot utilize the temporal information in videos. In this paper, we introduce the task of free-form open-ended video question answering. The open-ended answers enable wider applications compared with the common multiple-choice tasks in Visual-QA. We first propose a data set for open-ended Video-QA with the automatic question generation approaches. Then, we propose our sequential video attention and temporal question attention models. These two models apply the attention mechanism on videos and questions, while preserving the sequential and temporal structures of the guides. The two models are integrated into the model of unified attention. After the video and the question are encoded, the answers are generated wordwisely from our models by a decoder. In the end, we evaluate our models on the proposed data set. The experimental results demonstrate the effectiveness of our proposed model.

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