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.
By Danial Sharifrazi, Saadat Behzadi, Nouman Javed, Roohallah Alizadehsani, Prasad N. Paradkar, Asim Bhatti
arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
By Valentin Gabeff, Baptiste Maquignaz, Jennifer Shan, Sepideh Mamooler, Gencer Sumbul, Blair Costelloe, Devis Tuia, Alexander Mathis
VESSI is a Vision‑Language Model (VLM) framework that enhances automated video surveillance by converting salient visual features into textual descriptions for prompt‑driven interrogation. The authors evaluate four state‑of‑the‑art VLMs and introduce the Composite Model Utility Score (CMUS) to assess performance on largely unlabeled datasets. Results show that the best model flags relevant activity in over 66% of videos while cutting review time by more than 85%, and the CMUS ordering aligns with manual false‑positive rates, supporting its operational use.
By Saverio Cavasin, Pietro Tedeschi, Mattia Tamiazzo, Alessandro Brighente, Simone Milani, Mauro Conti
arXiv:2607. 16324v1 Announce Type: cross Abstract: Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without providing morphological evidence for their predictions, limiting the ability of microscopists to audit those predictions at the case level.
By Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza
arXiv:2609.25040v1 Announce Type: cross
Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...
By Sangam Kumar Jena, Pandarasamy Arjunan
arXiv:2608. 03112v1 Announce Type: cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge devices and in real-time surveillance applications.
By Paribesh Regmi, Qingshuang Chen, Chi Zhang, Heba Aly, Yelin Kim, Hongda Mao