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Found 15 papers, 5 papers with code
Question Aware Vision Transformer for Multimodal Reasoning
This integration results in dynamic visual features focusing on relevant image aspects to the posed question.
GRAM: Global Reasoning for Multi-Page VQA
The increasing use of transformer-based large language models brings forward the challenge of processing long sequences.
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition
Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text.
Towards Models that Can See and Read
Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from the text in the image.
Out-of-Vocabulary Challenge Report
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge.
Optical Character Recognition
Optical Character Recognition (OCR)
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Multimodal Semi-Supervised Learning for Text Recognition
In a novel setup, consistency is enforced on each modality separately.
On the Inversion of Deep Generative Models
Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.
On Calibration of Scene-Text Recognition Models
Although the topic of confidence calibration has been an active research area for the last several decades, the case of structured and sequence prediction calibration has been scarcely explored.
Sequence-to-Sequence Contrastive Learning for Text Recognition
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition.
When and How Can Deep Generative Models be Inverted?
Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.
Ada-LISTA: Learned Solvers Adaptive to Varying Models
Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance.
Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images.
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Parsimonious representations are ubiquitous in modeling and processing information.
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.
Multi-Layer Sparse Coding: The Holistic Way
The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN).
