Name
Abstract | ||
|---|---|---|
ABSTRACTMost of the existing works in supervised spatio-temporal video super-resolution (STVSR) heavily rely on a large-scale external dataset consisting of paired low-resolution low-frame rate (LR-LFR) and high-resolution high-frame-rate (HR-HFR) videos. Despite their remarkable performance, these methods make a prior assumption that the low-resolution video is obtained by down-scaling the high-resolution video using a known degradation kernel, which does not hold in practical settings. Another problem with these methods is that they cannot exploit instance-specific internal information of a video at testing time. Recently, deep internal learning approaches have gained attention due to their ability to utilize the instance-specific statistics of a video. However, these methods have a large inference time as they require thousands of gradient updates to learn the intrinsic structure of the data. In this work, we present Adaptive VideoSuper-Resolution (Ada-VSR) which leverages external, as well as internal, information through meta-transfer learning and internal learning, respectively. Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution. The model trained using our approach can quickly adapt to a specific video condition with only a few gradient updates, which reduces the inference time significantly. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3474085.3475320 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gupta Akash | 1 | 0 | 1.69 |
| Padmaja Jonnalagedda | 2 | 0 | 0.34 |
| B. Bhanu | 3 | 431 | 42.88 |
| Amit K. Roy Chowdhury | 4 | 1153 | 73.96 |