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Found 7 papers, 6 papers with code
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval
Sparse sampling is also likely to miss important frames corresponding to some text portions, resulting in textual redundancy.
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer.
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary.
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE.
TransAug: Translate as Augmentation for Sentence Embeddings
In this paper, we present TransAug (Translate as Augmentation), which provide the first exploration of utilizing translated sentence pairs as data augmentation for text, and introduce a two-stage paradigm to advances the state-of-the-art sentence embeddings.
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding.
