arXiv Computer Vision
Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 2
FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement
FiVOS is an interactive video object segmentation algorithm tailored for fish segmentation in aquaculture. It introduces a mask block filter and serial noise filters to detect and correct erroneous propagated masks early, improving accuracy and robustness. Experiments show FiVOS achieves state‑of‑the‑art performance on two newly constructed fish‑specific datasets.
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...
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.
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...
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
6d ago
MixSIS: Mixed-supervised instance segmentation
MixSIS introduces a mixed-supervised instance segmentation framework that permits a flexible combination of image, point, box, and mask annotations within a dataset, addressing the limitation of existing weakly-supervised methods that focus on a single annotation level. The authors present AnnsMixer, a baseline system that generates pseudo mask annotations from all annotation levels, even within a single image, and demonstrate its effectiveness on both WSIS and MixSIS tasks. Experiments and case studies show that MixSIS and AnnsMixer can reduce annotation time by up to 90% or improve segmentation results by 34% using simple annotation rules or heterogeneous data.