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CVPR 2022 Workshop on Fine-Grained Visual Categorization2022Fine-grained recognition and retrieval are complex tasks in computer vision due to the high level of similarity between images of different subclasses. Recent work on fine-grained image recognition achieved significant improvements by using the attention mechanisms of the Vision or Swin Transformers to find discriminative image regions at coarse or fine scales, respectively. Here, we propose SwinTransFuse
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ICWSM 20222022Online harassment in the form of hate speech has been on the rise in recent years. Addressing the issue requires a combination of content moderation by people, aided by automatic detection methods. As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech. Hate speech presents a challenge as it is directed at different
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SIGIR 20222022Many IR collections contain forbidden documents (𝐹 -docs), i.e. documents that should not be retrieved to the searcher. In an ideal scenario 𝐹 -docs are clearly flagged, hence the ranker can filter them out, guaranteeing that no 𝐹 -doc will be exposed. However, in real-world scenarios, filtering algorithms are prone to errors. Therefore, an IR evaluation system should also measure filtering quality in
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CHI 20222022Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel
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IJCNN 20222022Graph-based collaborative filtering for recommendation has attracted great attention recently, due to its effectiveness of capturing high-order proximity among users and items. To further improve its model robustness and alleviate label-sparsity issue, contrastive learning has been introduced to polish user and item representation by contrasting different views of user/item nodes, learning necessary and
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