Search Results for author:
Found 16 papers, 7 papers with code
Entity-aware Image Caption Generation
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images.
Paper Abstract Writing through Editing Mechanism
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract.
Ranked #1 on
Paper generation
on ACL Title and Abstract Dataset
Incorporating Background Knowledge into Video Description Generation
We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents.
Multilingual Entity, Relation, Event and Human Value Extraction
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.
Studying Wythoff and Zometool Constructions using Maple
We describe a Maple package that serves at least four purposes.
Computational Geometry
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
Cross-media Structured Common Space for Multimedia Event Extraction
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
GAIA: A Fine-grained Multimedia Knowledge Extraction System
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
Global Attention for Name Tagging
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited.
Learning from Lexical Perturbations for Consistent Visual Question Answering
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations.
Separating Skills and Concepts for Novel Visual Question Answering
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models.
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
We first enable abstention capabilities for several VQA models, and analyze both their coverage, the portion of questions answered, and risk, the error on that portion.
Segment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.
Ranked #2 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
Simple Token-Level Confidence Improves Caption Correctness
The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding.
Ranked #62 on
Visual Reasoning
on Winoground
Improving Selective Visual Question Answering by Learning from Your Peers
In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data.
