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Found 11 papers, 4 papers with code
Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey
Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system.
Non-autoregressive Sequence-to-Sequence Vision-Language Models
Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions.
DocFormerv2: Local Features for Document Understanding
We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU).
Ranked #9 on
Visual Question Answering (VQA)
on DocVQA test
(using extra training data)
Adaptive dynamic programming-based adaptive-gain sliding mode tracking control for fixed-wing UAV with disturbances
This paper proposes an adaptive dynamic programming-based adaptive-gain sliding mode control (ADP-ASMC) scheme for a fixed-wing unmanned aerial vehicle (UAV) with matched and unmatched disturbances.
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries
Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both the parts (subject, object, and predicate) and the sum (triplet as a whole) are to be detected in a hierarchical fashion.
Person Search by Text Attribute Query As Zero-Shot Learning
Existing person search methods predominantly assume the availability of at least one-shot imagery sample of the queried person.
Unsupervised Deep Learning by Neighbourhood Discovery
Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations.
Single-Label Multi-Class Image Classification by Deep Logistic Regression
The objective learning formulation is essential for the success of convolutional neural networks.
Imbalanced Deep Learning by Minority Class Incremental Rectification
In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data.
Class Rectification Hard Mining for Imbalanced Deep Learning
Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes
Recognising detailed clothing characteristics (fine-grained attributes) in unconstrained images of people in-the-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution).
