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Found 8 papers, 3 papers with code
Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales
Their ability to execute intricate compositional reasoning tasks is also constrained, culminating in a stagnation of learning progression for these models.
HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention
In this paper, we propose an end-to-end High-quality INpainting Transformer, abbreviated as HINT, which consists of a novel mask-aware pixel-shuffle downsampling module (MPD) to preserve the visible information extracted from the corrupted image while maintaining the integrity of the information available for high-level inferences made within the model.
INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries.
A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist.
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
The task divides into two steps: subject suggestion, the task of populating the main column; and gap filling, the task of populating the remaining columns.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on
Knowledge Base Question Answering
on GrailQA
Improving Entity Linking by Modeling Latent Entity Type Information
Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
(Micro-F1 metric)
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
