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Found 6 papers, 3 papers with code
Training-Free Unsupervised Prompt for Vision-Language Models
In light of this, we propose Training-Free Unsupervised Prompts (TFUP), which maximally preserves the inherent representation capabilities and enhances them with a residual connection to similarity-based prediction probabilities in a training-free and labeling-free manner.
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering
To solve the problems, we fine-tune PLMs by leveraging the frequency information of words and propose a novel USRL framework, namely Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering (SLT-FAI).
Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models
A contrastive loss is employed to align such augmented text and image representations on downstream tasks.
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning.
Ranked #4 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
Next-item Recommendations in Short Sessions
The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session.
Hyperbolic Node Embedding for Signed Networks
Signed network embedding methods aim to learn vector representations of nodes in signed networks.
