Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection
Authors: Robert James Brock, Sebastian Maximilian Krupa, Jason Kahei Tam
Organizations: Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
Abstract
The FathomNetCLEF 2026 competition combines underwater object detection and fine-grained marine species classification under a positive-unlabeled evaluation setting. The provided training labels are sparse, while the hidden test set is out-of-distribution relative to the training imagery, creating both annotation incompleteness and source-shift challenges. We describe DS@GT ARC's multi-stage system developed for this setting while keeping model training restricted to the data provided by the competition. The final private-leaderboard model uses a frozen Megalodon YOLOv8x detector as a class-agnostic proposal generator, combines global and tiled inference with tile-edge filtering, classifies expanded proposal crops with a LoRA-finetuned DINOv3 ViT-H classifier, and ranks predictions using weighted geometric fusion of detector and classifier confidence. This system placed 12th out of 102 teams. A closely related variant added a locally trained TTN-inspired validity head as a light reranking signal, improving public-leaderboard and proxy-evaluation performance but slightly reducing private-leaderboard performance. Across experiments, the strongest lesson was that train-derived validation and detector-only metrics were not reliable enough for model selection. Instead, we used proxy datasets only for validation and comparison, and combined those signals with leaderboard feedback and targeted ablations. These experiments showed that reserving proposal recall, avoiding over-aggressive filtering, and improving downstream ranking were more effective than fine-tuning the detector or directly training on noisy pseudo-labels. Code: https://github.com/dsgt-arc/fathomnetclef-2026.
Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining a taxonomy-weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and independent per-rank classification heads. Evaluated on the FathomNet 2025 dataset1 (79 marine classes across seven taxonomic ranks), the system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution (1.535), with the largest gains from metric-aligned inference and simple, decoupled components that generalize better than learned dependencies under distribution shift.
Dan Zimmerman, Dimitris A. Pados, George Sklivanitis
Fine-grained recognition of marine organisms is important for ecological research, biodiversity monitoring, and habitat conservation. However, existing methods often focus on the target organism alone, which can overlook informative cues from the surrounding environment. Moreover, biological taxonomy is often underused during model training, despite providing meaningful hierarchical information for distinguishing visually similar taxa. To address these limitations, we propose MATANet, a Multi-Context Attention and Taxonomy-Aware Network for ROI-guided fine-grained marine organism recognition. Inspired by expert taxonomic identification, MATANet jointly models target appearance, environmental context, and taxonomic information. Specifically, MCEAM uses the target organism representation as a query to attend to informative cues from surrounding regions. In addition, biological taxonomy is used as auxiliary supervision through level-wise taxonomic CE, encouraging predictions that are more consistent with the taxonomic hierarchy. Experiments on FathomNet 2025 and FishCLEF2015 demonstrate that MATANet consistently outperforms representative benchmark models for fine-grained marine organism recognition. The official challenge evaluation further supports the practical effectiveness of the proposed framework on held-out test data. Additional evaluations on FAIR1M v2.0 assess the transferability of MATANet to another visual recognition domain, while experiments using automatically detected ROIs examine its practical applicability and robustness in detection-based recognition settings.
Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However, detector performance is typically evaluated using aggregate metrics that hide failures in specific environments, while existing domain generalization benchmarks often rely on synthetic variations that do not reflect real-world conditions. We introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels. Using this framework, we perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance, revealing substantial domain-dependent discrepancies. By incorporating physically meaningful domain labels, domain shift becomes something we can characterize, measure, benchmark, and act on. We highlight how this can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.