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Identifying Semantically Difficult Samples to Improve Text Classification
In this paper, we investigate the effect of addressing difficult samples from a given text dataset on the downstream text classification task.
Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning
Recent approaches in literature have exploited the multi-modal information in documents (text, layout, image) to serve specific downstream document tasks.
Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data
The next stage tackles under-segmentations using a combination of a novel ``cut-point'' based technique and localized second-level edge detection to obtain individual parcels.
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