Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive original frames. Most existing methods simply linearly combine the warped frames, leading to a loss of image texture. Since moving objects usually have similarities in consecutive frames, we propose a similarity-aware video frame interpolation method (SAIN) that searches patches with similar texture in the embedding space from input frames to extract features and capture image details. To gather the frame details and restore image texture, SAIN incorporates an implicit neural representation learning from similar patches to enrich image details and refine outputs in frame synthesis networks. Experiments demonstrate that SAIN preserves image texture and enhances interpolated image quality significantly.

Deep convolutional neural networks (CNNs) have been widely and successfully applied in many computer vision tasks, such as classification, detection, semantic segmentation, and so on. As for image retrieval, while off-the-shelf CNN features from models trained for classification task are demonstrated promising, it remains a challenge to learn specific features oriented for instance retrieval. Witnessing the great success of low-level SIFT feature in image retrieval and its complementary nature to the semantic-aware CNN feature, in this paper, we propose to embed the SIFT feature into the CNN feature with a Siamese structure in a learning-based paradigm. The learning objective consists of two kinds of loss, i.e., similarity loss and fidelity loss. The first loss embeds the image-level nearest neighborhood structure with the SIFT feature into CNN feature learning, while the second loss imposes that the CNN feature with the updated CNN model preserves the fidelity of that from the original CNN model solely trained for classification. After the learning, the generated CNN feature inherits the property of the SIFT feature, which is well oriented for image retrieval. We evaluate our approach on the public data sets, and comprehensive experiments demonstrate the effectiveness of the proposed method.