Illegal, Unreported, and Unregulated (IUU) fishing aggravates the global crisis caused by overfishing, threatening the sustainability of marine ecosystems and fisheries worldwide. Distinctive operational characteristics of fishing vessels result in unique footprints on marine environments and socio-economic structures, depending on their fishing method and gear type such as trawlers with non-selective gear that disrupts the seabed, purse seiners using Fish Aggregating Devices (FADs), and longliners notorious for high bycatch rates. As these vessels play an essential role in commercial fishing and the industry, effective monitoring, regulation, and enforcement are critical to mitigate the devastating consequences of overfishing and promote sustainable fishing practices. To this end, this paper introduces a novel multi-stage method for Gear type Identification by Spatiotemporal trajectory Transformation (GIST). This method proposes a data-centric approach that employs domain knowledge to facilitate the deployment of an efficient and accurate analysis of operational patterns of fishing vessels derived from Automatic Identification System (AIS) data. Our method first extracts fishing patterns from vessel trajectories to refine data integrity and isolate only the most relevant activities, thereby ensuring a more accurate result. Next, it encapsulates the distributional insights of fishing activities into fixed-sized "images" as actionable input for a multi-class CNN-based classifier. Utilizing GIST bypasses complicated linear analyses of time series data for rendering lengthy trajectories, advancing an efficient gear type identification with 97% accuracy. To the best of our knowledge, GIST is the first to use a multi-stage method to distinguish three principal gear types widely used globally. Our experiments confirm GIST's practicability and effectiveness, marking a significant advancement towards stricter enforcement of regulations in the fight against IUU fishing.

Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep Neural Networks to represent the minority labels and avoid bias towards majority labels. The extensive body of imbalanced approaches address categorical label spaces but fail to effectively extend to regression problems where the label space is continuous. Local and global correlations among continuous labels provide valuable insights towards effectively modelling relationships in feature space. In this work, we propose ConR, a contrastive regularizer that models global and local label similarities in feature space and prevents the features of minority samples from being collapsed into their majority neighbours. ConR discerns the disagreements between the label space and feature space, and imposes a penalty on these disagreements. ConR minds the continuous nature of label space with two main strategies in a contrastive manner: incorrect proximities are penalized proportionate to the label similarities and the correct ones are encouraged to model local similarities. ConR consolidates essential considerations into a generic, easy-to-integrate, and efficient method that effectively addresses deep imbalanced regression. Moreover, ConR is orthogonal to existing approaches and smoothly extends to uni- and multi-dimensional label spaces. Our comprehensive experiments show that ConR significantly boosts the performance of all the state-of-the-art methods on four large-scale deep imbalanced regression benchmarks.

Unsupervised domain adaptation is the problem of transferring extracted knowledge from a labeled source domain to an unlabeled target domain. To achieve discriminative domain adaptation recent studies take advantage of target sample pseudo-labels to impose class-aware distribution alignment across the source and target domains. Still, they have some shortcomings such as making decisions based on inaccurate pseudo-labeled samples that mislead the adaptation process. In this paper, we propose a progressive deep feature alignment, called Norma, to tackle class-aware unsupervised domain adaptation for image classification by enforcing inter-class compactness and intra-class discrepancy through a hybrid learning process. To this end, Norma's optimization process is defined based on a novel triplet loss which not only addresses soft prototype alignment but also pushes away multiple negative centroids. Also, to extract maximum discriminative domain knowledge per iteration, we propose a joint positive and negative learning procedure along with an uncertainty-guided progressive pseudo-labeling on the basis of prototype-based clustering and conditional probability. Our experimental results on several benchmarks demonstrate that Norma outperforms the state-of-the-art methods.