13 citations · 31 across the 11 of their papers we have counts for
11 papers
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Lexiang Tang, Weihao Gao, Bingchen Zhao +4
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However,…
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
Bang Yang, Yong Dai, Xuxin Cheng +3
While vision-language pre-trained models (VL-PTMs) have advanced multimodal research in recent years, their mastery in a few languages like English restricts their applicability in…
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
Bang Yang, Fenglin Liu, Xian Wu +3
Supervised visual captioning models typically require a large scale of images or videos paired with descriptions in a specific language (i.e., the vision-caption pairs) for trainin…
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels
Bang Yang, Fenglin Liu, Zheng Li +4
Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation appr…
Customizing General-Purpose Foundation Models for Medical Report Generation
Bang Yang, Asif Raza, Yuexian Zou +1
Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given med…
Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation
Yaowei Li, Bang Yang, Xuxin Cheng +3
Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establis…
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation
Bang Yang, Fenglin Liu, Yuexian Zou +3
Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly…
Graph-in-Graph Network for Automatic Gene Ontology Description Generation
Fenglin Liu, Bang Yang, Chenyu You +4
Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes wit…
CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter
Bang Yang, Tong Zhang, Yuexian Zou
For video captioning, "pre-training and fine-tuning" has become a de facto paradigm, where ImageNet Pre-training (INP) is usually used to encode the video content, then a task-orie…
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Fenglin Liu, Xuancheng Ren, Xian Wu +4
Video captioning combines video understanding and language generation. Different from image captioning that describes a static image with details of almost every object, video capt…
Non-Autoregressive Coarse-to-Fine Video Captioning
Bang Yang, Yuexian Zou, Fenglin Liu +1
It is encouraged to see that progress has been made to bridge videos and natural language. However, mainstream video captioning methods suffer from slow inference speed due to the…