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Found 15 papers, 6 papers with code
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization
One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role.
DICTDIS: Dictionary Constrained Disambiguation for Improved NMT
In this work we present \dictdis, a lexically constrained NMT system that disambiguates between multiple candidate translations derived from dictionaries.
COSMic: A Coherence-Aware Generation Metric for Image Descriptions
Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations.
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training.
Ranked #9 on
Image Captioning
on nocaps-val-out-domain
Understanding Guided Image Captioning Performance across Domains
Image captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativeness, and information overload.
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models
Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data.
Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning.
Clue: Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning.
Reinforcing an Image Caption Generator Using Off-Line Human Feedback
Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation is a few overall statistics over the evaluation dataset.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Ranked #1 on
Natural Language Inference
on QNLI
Neural Naturalist: Generating Fine-Grained Image Comparisons
We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds.
Quality Estimation for Image Captions Based on Large-scale Human Evaluations
Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild.
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering
Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering.
Ranked #4 on
Visual Question Answering (VQA)
on VizWiz 2018
Informative Image Captioning with External Sources of Information
An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact.
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles.
