arXiv Computer Vision
WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
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arXiv AI
6d ago
MarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding
arXiv:2610.33478v1 Announce Type: cross Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, i...
arXiv Computer Vision
Oct 22
Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
arXiv:2610.29047v1 Announce Type: new Abstract: Understanding the properties of fish populations is increasingly important. Analyzing fish length, width, and mass is integral to maintaining healthy s...
arXiv Machine Learning
Aug 19
Leveraging existing sparse point annotations for benthic imagery dense segmentation
The paper presents a method that leverages sparse expert point annotations from historical benthic surveys to improve dense segmentation of marine imagery. By using these points as visual prompts for the SAM2 foundation model and introducing a mechanism to filter out unreliable points, the authors generate high‑quality pseudo‑ground‑truth masks that train more accurate fine‑grained semantic segmentation models. The approach is validated on public benthic datasets and a new benchmark featuring real‑world sparse annotations, aiming to enable scalable ecological analysis.
arXiv Computer Vision
Oct 1
TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos
arXiv:2609.38347v1 Announce Type: new Abstract: Quantifying collective fish behavior requires accurate trajectories, yet multi-view 3D tracking remains challenging due to frequent occlusions, visuall...
arXiv AI
Jul 28
MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning
arXiv:2607. 24064v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) have achieved remarkable success in visual understanding, driven by the growing availability of high-quality image-text pairs.
arXiv Computer Vision
Sep 14
CoralscapesV2: Panoptic and Fine-Grained Visual Scene Understanding in Coral Reefs
CoralscapesV2 is an expanded dataset for coral reef visual scene understanding, increasing the number of fine‑grained classes from 39 to 95 and adding 65,000 exhaustive fish instance masks. It supports panoptic segmentation by providing high‑quality semantic and instance labels across diverse, unconstrained reef imagery. The dataset serves as a challenging benchmark for modern segmentation models and enables broader applications such as benthic cover mapping and automated fish‑reef interaction analysis.