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Found 10 papers, 3 papers with code
Progressive Multi-modal Conditional Prompt Tuning
Initialization is responsible for encoding image and text using a VLM, followed by a feature filter that selects text features similar to image.
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator
To efficiently transfer soft prompt, we propose a novel framework, Multilingual Prompt Translator (MPT), where a multilingual prompt translator is introduced to properly process crucial knowledge embedded in prompt by changing language knowledge while retaining task knowledge.
Text-Only Training for Visual Storytelling
Visual storytelling aims to generate a narrative based on a sequence of images, necessitating both vision-language alignment and coherent story generation.
Fine-grained Semantic Alignment Network for Weakly Supervised Temporal Language Grounding
In this work, we propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG.
Geometric Representation Learning for Document Image Rectification
In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one.
DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction
Specifically, DocTr consists of a geometric unwarping transformer and an illumination correction transformer.
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i. e., NMFs-CSL, SLR500, MSASL and WLASL.
Ranked #1 on
Sign Language Recognition
on WLASL100
(using extra training data)
Weakly Supervised Temporal Adjacent Network for Language Grounding
To this end, we introduce a novel weakly supervised temporal adjacent network (WSTAN) for temporal language grounding.
Pre-training Text Representations as Meta Learning
In this work, we introduce a learning algorithm which directly optimizes model's ability to learn text representations for effective learning of downstream tasks.
Asking Clarification Questions in Knowledge-Based Question Answering
We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation.
