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Found 4 papers, 1 papers with code
Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching
To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models.
Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting the sentiments residing in the utterance, video and audio, which are vital clues for sarcasm explanation.
Unsupervised Temporal Action Localization via Self-paced Incremental Learning
Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance.
Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection
In this paper, we improve the performance of one-stage methods by enabling them to extract disentangled interaction representations.
