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Found 12 papers, 3 papers with code
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos.
Ranked #3 on
Video Question Answering
on MVBench
Unsupervised Extractive Summarization with Learnable Length Control Strategies
Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption.
Prompt-Based Length Controlled Generation with Reinforcement Learning
Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks.
Enhancing Coherence of Extractive Summarization with Multitask Learning
This study proposes a multitask learning architecture for extractive summarization with coherence boosting.
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript.
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding
While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far.
Lexicon-injected Semantic Parsing for Task-Oriented Dialog
Then we present a novel lexicon-injected semantic parser, which collects slot labels of tree representation as a lexicon, and injects lexical features to the span representation of parser.
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information
In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS.
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks
In this paper, we design a novel unified parameter-efficient transfer learning framework that works effectively on both pure language and V&L tasks.
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Experiments show that Wukong can serve as a promising Chinese pre-training dataset and benchmark for different cross-modal learning methods.
Ranked #6 on
Image Retrieval
on MUGE Retrieval
UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation
Specially, we adopt knowledge distillation from a vision-language pretrained model to improve image selection, which avoids any requirement on the existence and quality of image captions.
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
Adversarial attacking aims to fool deep neural networks with adversarial examples.
