Search Results for author:
Found 9 papers, 4 papers with code
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning
For task completion, the agent needs to align and integrate various navigation modalities, including instruction, observation and navigation history.
ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks
Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs).
A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training
These two steps are iteratively performed in our framework for continuous learning.
MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
In MVPTR, we follow the nested structure of both modalities to introduce concepts as high-level semantics.
Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval
Existing research for image text retrieval mainly relies on sentence-level supervision to distinguish matched and mismatched sentences for a query image.
TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning
Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN.
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information
In this paper, we focus on the problem of unsupervised image-sentence matching.
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolutional neural networks and recurrent neural networks).
