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Found 5 papers, 3 papers with code
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
The Composed Image Retrieval (CIR) task aims to retrieve target images using a composed query consisting of a reference image and a modified text.
Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval
Furthermore, our method can also boost the uni-modal retrieval performance of image-text retrieval models, enabling it to achieve universal retrieval.
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the Gradient Dissipation, the Weight, and the Ratio.
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
An intriguing phenomenon in CSE is the significant performance gap between supervised and unsupervised methods, with their only difference lying in the training data.
Code-Style In-Context Learning for Knowledge-Based Question Answering
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical applications.
