The submission is being made as a working note as part of the Offensive Language Identification in Dravidian Languages shared task. The proposed model “DrOLIC” uses IndicBERT and a simple 4-layered MLP to do the multiclass classification... more
The submission is being made as a working note as part of the Offensive Language Identification in Dravidian Languages shared task. The proposed model “DrOLIC” uses IndicBERT and a simple 4-layered MLP to do the multiclass classification problem and we achieved an F1 score of 0.85 on the Malayalam dataset.
State of the art architectures for untrimmed video Temporal Action Localization (TAL) have only considered RGB and Flow modalities, leaving the information-rich audio modality totally unexploited. Audio fusion has been explored for the... more
State of the art architectures for untrimmed video Temporal Action Localization (TAL) have only considered RGB and Flow modalities, leaving the information-rich audio modality totally unexploited. Audio fusion has been explored for the related but arguably easier problem of trimmed (clip-level) action recognition. However, TAL poses a unique set of challenges. In this paper, we propose simple but effective fusion-based approaches for TAL. To the best of our knowledge, our work is the first to jointly consider audio and video modalities for supervised TAL. We experimentally show that our schemes consistently improve performance for state of the art video-only TAL approaches. Specifically, they help achieve new state of the art performance on large-scale benchmark datasets ActivityNet1.3 (54.34 mAP@0.5) and THUMOS14 (57.18 mAP@0.5). Our experiments include ablations involving multiple fusion schemes, modality combinations and TAL architectures. Our code, models and associated data are...
