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Found 12 papers, 3 papers with code
Continual Learning of Object Instances
Our extensive experiments on three large-scale datasets, using two different architectures for five different continual learning methods, reveal that Normalised cross-entropy and synthetic transfer leads to less forgetting in existing techniques.
Locality-Aware Hyperspectral Classification
Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wide continuum of spectra.
Ranked #1 on
Hyperspectral Image Classification
on Houston
(OA@15perclass metric)
Towards Label-Efficient Incremental Learning: A Survey
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence.
Re-evaluating Automatic Metrics for Image Captioning
The task of generating natural language descriptions from images has received a lot of attention in recent years.
Diagnosing Rarity in Human-Object Interaction Detection
To that end, in this paper, we propose to diagnose rarity in HOI detection.
Self-Selective Context for Interaction Recognition
Our experiments show that SSC leads to an important increase in interaction recognition performance, while using much fewer parameters.
Structured Visual Search via Composition-aware Learning
The structure is in the form of a 2D composition that encodes the position and the category of the objects.
Human-Object Interaction Detection via Weak Supervision
iii) We evaluate Align-Former on HICO-DET [5] and V-COCO [13], and show that Align-Former outperforms existing image-level supervised HO-I detectors by a large margin (4. 71% mAP improvement from 16. 14% to 20. 85% on HICO-DET [5]).
Are Labels Needed for Incremental Instance Learning?
We propose VINIL, a self-incremental learner that can learn object instances sequentially, ii.
Adaptive Regularization for Class-Incremental Learning
Class-Incremental Learning updates a deep classifier with new categories while maintaining the previously observed class accuracy.
What Can AutoML Do For Continual Learning?
This position paper outlines the potential of AutoML for incremental (continual) learning to encourage more research in this direction.
