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Found 13 papers, 5 papers with code
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
Ranked #1 on
Image Captioning
on COCO Captions
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types
In this paper, we present WikiDiverse, a high-quality human-annotated MEL dataset with diversified contextual topics and entity types from Wikinews, which uses Wikipedia as the corresponding knowledge base.
Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding
Moreover, since the backbones are query-agnostic, it is difficult to completely avoid the inconsistency issue by training the visual backbone end-to-end in the visual grounding framework.
Achieving Human Parity on Visual Question Answering
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Ranked #11 on
Visual Question Answering
on VQA v2 test-dev
Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training
Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion.
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion
We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images.
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
However, due to the variety of users{'} emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain.
Multi-Domain Dialogue Acts and Response Co-Generation
Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed.
Attention Optimization for Abstractive Document Summarization
Attention plays a key role in the improvement of sequence-to-sequence-based document summarization models.
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task.
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce.
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features.
Cross-Lingual Semantic Textual Similarity
Machine Translation
+1
