arXiv Computer Vision
Deep Learning Approach for RealTime Contraction Detection in Laboring Dairy Cattle
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arXiv AI
Jun 16
Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review
arXiv:2606. 15655v1 Announce Type: new Abstract: The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management.
arXiv AI
Jun 15
CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
arXiv AI
Aug 13
A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
arXiv:2608. 11582v1 Announce Type: cross Abstract: Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background.
arXiv Computer Vision
Sep 22
Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture
arXiv:2609.23397v1 Announce Type: new Abstract: Shrimp diseases continue to cause devastating losses in the aquaculture industry, driving a critical need for robust, automated detection. This work co...
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.
Hugging Face Trending Papers
Jul 13
Temporal Feature Distillation for Label-Efficient Precise Event Spotting in Sports Videos
Precise Event Spotting (PES) requires distinguishing visually similar yet semantically distinct adjacent frames, making it fundamentally different from image classification and coarse action recognition. Although self-distillation methods such as DINO have shown strong representation learning ability in images, we find that directly applying them to PES is ineffective: without supervised guidance, subtle but crucial motion cues are often suppressed as noise, leading to representations that are insensitive to precise event boundaries.