Long-tailed Video Recognition

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The rise of deep learning (DL) and the availability of publiclarge-scale vision datasets such as ImageNet [1] have led to significant progress inmodern day computer vision. However, knowledge transfer of DL models frompublic datasets to real world usecases faces multiple challenges due to issuessuch as differences in domains, data scarcity etc. Two of the most significantchallenges are :DL models trained on public datasets give suboptimal performance in novel environments or domains. Therefore they either need to be retrained from scratch or fine-tuned for each domain.Re-training DL models from scratch or fine-tuning them in novel domains may still provide sub-optimal and biased performance due to long-tailed nature of some domains.These shortcomings have stirred up strong research motivations and are popularly known as problems of 'domain adaptation' and 'long-tailed recognition' respectively. Even though there have been various works in these fields, most of them concern with image-based problems. However, the above shortcomings appear in video recognition as well and have been scarcely explored.