arXiv Computer Vision
Aug 24

WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition

arXiv:2608.21281v1 Announce Type: new Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...

By Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin, Ziang Liu, Max Whitton, Madelyn Hair, Liam Gutierrez, Haozheng Yu, Kristin Branson, Vivek Jayaraman, Michael A. Gil, Andrew M. Hein, Jennifer J. Sun
Hugging Face Trending Papers
Aug 19

Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance

The paper introduces Molmo2Fish, an interactive tool that uses a multimodal large language model to correct imperfect fish tracking predictions in sonar datasets. It demonstrates that the system can effectively improve multi‑object tracking performance through natural language guidance, though further enhancements are needed. The authors provide open‑source code and data for replication.

arXiv Computer Vision
Sep 7

PuTR-CouT: Counting-by-Tracking in Camera-Trap Image Sequences

PuTR-CouT is a transformer‑based counting‑by‑tracking framework designed for camera‑trap image sequences. It generates synthetic training data using structural priors to create pseudo‑tracking labels, enabling the tracker to associate detections across frames and estimate per‑species counts. The method improves upon the MaxBoxCount baseline on the iWildCam 2021 benchmark, offering competitive counting results along with multi‑species predictions and track‑level verification.

By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
arXiv AI
Jun 10

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

arXiv:2606. 10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles.

By Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver, Katie Appleby, Sarah Beatham, Naomi Davies Walsh, Stuart Nixon, Naomi Matthews, Chris Sutherland, Kelly Hitchcock
arXiv AI
Jun 17

A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras

arXiv:2510. 23798v2 Announce Type: replace-cross Abstract: The proliferation of floating anthropogenic debris in rivers has emerged as a pressing environmental concern, exerting a detrimental influence on biodiversity, water quality, and human activities such as navigation and recreation.

By Gauthier Grimmer, Romain Wenger, Cl\'ement Flint, Germain Forestier, Gilles Rixhon, Valentin Chardon