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Found 7 papers, 1 papers with code
CPN: Complementary Proposal Network for Unconstrained Text Detection
Existing methods for scene text detection can be divided into two paradigms: segmentation-based and anchor-based.
Center Contrastive Loss for Metric Learning
In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss.
Ranked #5 on
Metric Learning
on CUB-200-2011
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Contrastive learning-based video-language representation learning approaches, e. g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs.
Ranked #8 on
Video Question Answering
on MSRVTT-QA
Deep Self-Adaptive Hashing for Image Retrieval
Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning.
A pooling based scene text proposal technique for scene text reading in the wild
Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding.
WeText: Scene Text Detection under Weak Supervision
With a "light" supervised model trained on a small fully annotated dataset, we explore semi-supervised and weakly supervised learning on a large unannotated dataset and a large weakly annotated dataset, respectively.
Text Flow: A Unified Text Detection System in Natural Scene Images
With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively.
