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Found 7 papers, 2 papers with code
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
To alleviate CF raised by covariate shift and lexical overlap, we further propose a novel approach that ensures the identical distribution of all token embeddings during initialization and regularizes token embedding learning during training.
CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query.
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory
Due to two annoying issues in video grounding: (1) the co-existence of some visual entities in both ground truth and other moments, \ie semantic overlapping; (2) only a few moments in the video are annotated, \ie sparse annotation dilemma, vanilla contrastive learning is unable to model the correlations between temporally distant moments and learned inconsistent video representations.
Efficient Multimodal Fusion via Interactive Prompting
Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era.
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists.
Exploiting Auxiliary Caption for Video Grounding
Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video.
SIAD: Self-supervised Image Anomaly Detection System
To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.
