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Found 15 papers, 7 papers with code
Are Visual-Linguistic Models Commonsense Knowledge Bases?
We seek to determine the capability of pretrained visual-linguistic models as knowledge sources on demand.
Implicit Affordance Acquisition via Causal Action-Effect Modeling in the Video Domain
Affordance knowledge is a fundamental aspect of commonsense knowledge.
On the Complementarity of Images and Text for the Expression of Emotions in Social Media
The emotions of anger and sadness are best predicted with a multimodal model, while text alone is sufficient for disgust, joy, and surprise.
SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation
Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation.
Humans Meet Models on Object Naming: A New Dataset and Analysis
We also find that standard evaluations underestimate the actual effectiveness of the naming model: on the single-label names of the original dataset (Visual Genome), it obtains −27{\%} accuracy points than on MN v2, that includes all valid object names.
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers.
Ranked #1 on
Referring Expression Segmentation
on A2Dre test
Object Naming in Language and Vision: A Survey and a New Dataset
We highlight the challenges involved and provide a preliminary analysis of the ManyNames data, showing that there is a high level of agreement in naming, on average.
Recurrent Instance Segmentation using Sequences of Referring Expressions
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions).
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4).
Grounding Semantic Roles in Images
We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation.
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues.
