Anticipation of per-pixel semantics in a future unobserved frame is also known as dense semantic forecasting. State-of-the-art methods are based on single-level regression subsampled abstract representation recognition model. However, cannot account for skip connections from the backbone to upsampling path. We propose address this shortcoming by warping shallow features observed images with ups...

Video semantic segmentation is active in recent years benefited from the great progress of image segmentation. For such a task, per-frame generally unacceptable practice due to high computation cost. To tackle this issue, many works use flow-based feature propagation reuse features previous frames. However, optical flow estimation inevitably suffers inaccuracy and then causes propagated distort...

Generalized zero-shot learning (GZSL) is a technique to train deep model identify unseen classes using the attribute. In this paper, we put forth new GZSL that improves classification performance greatly. Key idea of proposed approach, henceforth referred as semantic feature extraction-based (SE-GZSL), use containing only attribute-related information in relationship between image and doing so,...

In order to improve the accuracy of short text similarity calculation, this paper presents the idea that use the history of short text messages to construct semantic feature space, then use the vector in semantic feature space to represent short text and do semantic extension, and finally calculate the short text similarity of corresponding vector in the semantic feature space. This method can ...