The extensive rise in consumption of online social media (OSMs) by a large number of people poses a critical problem of curbing the spread of hateful content on these platforms. With the growing usage of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The subtle variations of code-mixed texts along with switching scripts only add to the complexity. This paper presents a solution for the HASOC 2021 Multilingual Twitter Hate-Speech Detection challenge by team PreCog IIIT Hyderabad. We adopt a multilingual transformer based approach and describe our architecture for all 6 sub-tasks as part of the challenge. Out of the 6 teams that participated in all the sub tasks, our submissions rank 3rd overall.
is a self-supervised method that learns to Canonicalize the 3D orientation and position (3D pose) for full and partial shapes. (left) Our method takes un-canonicalized 3D point clouds (gray) from different categories as input and produces consistently canonicalized outputs (colored). (right) Our method can also operate on partial point clouds (missing part of shape shown only for visualization). In addition, ConDor can also learn consistent co-segmentation of shapes without supervision, visualized as colored parts.
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