TaE: Task-aware Expandable Representation for Long Tail Class Incremental Learning

Class-incremental learning (CIL) aims to train classifiers that learn new classes without forgetting old ones. Most CIL methods focus on balanced data distribution for each task, overlooking real-world long-tailed distributions. Therefore, Long-Tailed Class-Incremental Learning (LT-CIL) has been introduced, which trains on data where head classes have more samples than tail classes. Existing methods mainly focus on preserving representative samples from previous classes to combat catastrophic forgetting. Recently, dynamic network algorithms frozen old network structures and expanded new ones, achieving significant performance. However, with the introduction of the long-tail problem, merely extending task-specific parameters can lead to miscalibrated predictions, while expanding the entire model results in an explosion of memory size. To address these issues, we introduce a novel Task-aware Expandable (TaE) framework, dynamically allocating and updating task-specific trainable parameters to learn diverse representations from each incremental task, while resisting forgetting through the majority of frozen model parameters. To further encourage the class-specific feature representation, we develop a Centroid-Enhanced (CEd) method to guide the update of these task-aware parameters. This approach is designed to adaptively minimize the distances between intra-class features while simultaneously maximizing the distances between inter-class features across all seen classes. The utility of this centroid-enhanced method extends to all "training from scratch" CIL algorithms. Extensive experiments were conducted on CIFAR-100 and ImageNet100 under different settings, which demonstrates that TaE achieves state-of-the-art performance.

Global Wheat Head Detection dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods

Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed methods for wheat head detection from high-resolution RGB imagery. They are based on computer vision and machine learning and are generally calibrated and validated on limited datasets. However, variability in observational conditions, genotypic differences, development stages, head orientation represents a challenge in computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse and well-labelled dataset, the Global Wheat Head detection (GWHD) dataset. It contains 4,700 high-resolution RGB images and 190,000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD is publicly available at http://www.global-wheat.com/ and aimed at developing and benchmarking methods for wheat head detection.