Zobrazeno 1 - 10
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pro vyhledávání: '"Stewart, Charles A."'
Autor:
Mai, Zheda, Chowdhury, Arpita, Zhang, Ping, Tu, Cheng-Hao, Chen, Hong-You, Pahuja, Vardaan, Berger-Wolf, Tanya, Gao, Song, Stewart, Charles, Su, Yu, Chao, Wei-Lun
Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing valuable knowledge the model had learned in pre-training. For example,
Externí odkaz:
http://arxiv.org/abs/2409.16223
Autor:
Maruf, M., Daw, Arka, Mehrab, Kazi Sajeed, Manogaran, Harish Babu, Neog, Abhilash, Sawhney, Medha, Khurana, Mridul, Balhoff, James P., Bakis, Yasin, Altintas, Bahadir, Thompson, Matthew J., Campolongo, Elizabeth G., Uyeda, Josef C., Lapp, Hilmar, Bart, Henry L., Mabee, Paula M., Su, Yu, Chao, Wei-Lun, Stewart, Charles, Berger-Wolf, Tanya, Dahdul, Wasila, Karpatne, Anuj
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language mo
Externí odkaz:
http://arxiv.org/abs/2408.16176
Autor:
Khurana, Mridul, Daw, Arka, Maruf, M., Uyeda, Josef C., Dahdul, Wasila, Charpentier, Caleb, Bakış, Yasin, Bart Jr., Henry L., Mabee, Paula M., Lapp, Hilmar, Balhoff, James P., Chao, Wei-Lun, Stewart, Charles, Berger-Wolf, Tanya, Karpatne, Anuj
A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale ima
Externí odkaz:
http://arxiv.org/abs/2408.00160
Autor:
Kline, Jenna M., Kholiavchenko, Maksim, Brookes, Otto, Berger-Wolf, Tanya, Stewart, Charles V., Stewart, Christopher
In situ imageomics leverages machine learning techniques to infer biological traits from images collected in the field, or in situ, to study individuals organisms, groups of wildlife, and whole ecosystems. Such datasets provide real-time social and e
Externí odkaz:
http://arxiv.org/abs/2407.16864
Autor:
Duporge, Isla, Kholiavchenko, Maksim, Harel, Roi, Wolf, Scott, Rubenstein, Dan, Crofoot, Meg, Berger-Wolf, Tanya, Lee, Stephen, Barreau, Julie, Kline, Jenna, Ramirez, Michelle, Stewart, Charles
Using drones to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding group primate behavior. Previous studies have demonstrated that it is possible to automate the classification of pr
Externí odkaz:
http://arxiv.org/abs/2405.17698
Autor:
Algasov, Aleksandr, Nepovinnykh, Ekaterina, Eerola, Tuomas, Kälviäinen, Heikki, Stewart, Charles V., Otarashvili, Lasha, Holmberg, Jason A.
Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual
Externí odkaz:
http://arxiv.org/abs/2405.15976
Autor:
Pahuja, Vardaan, Luo, Weidi, Gu, Yu, Tu, Cheng-Hao, Chen, Hong-You, Berger-Wolf, Tanya, Stewart, Charles, Gao, Song, Chao, Wei-Lun, Su, Yu
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalization to new and unseen locations. Images are typically associated with d
Externí odkaz:
http://arxiv.org/abs/2401.00608
Autor:
Stevens, Samuel, Wu, Jiaman, Thompson, Matthew J, Campolongo, Elizabeth G, Song, Chan Hee, Carlyn, David Edward, Dong, Li, Dahdul, Wasila M, Stewart, Charles, Berger-Wolf, Tanya, Chao, Wei-Lun, Su, Yu
Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for
Externí odkaz:
http://arxiv.org/abs/2311.18803
Autor:
Paul, Dipanjyoti, Chowdhury, Arpita, Xiong, Xinqi, Chang, Feng-Ju, Carlyn, David, Stevens, Samuel, Provost, Kaiya L., Karpatne, Anuj, Carstens, Bryan, Rubenstein, Daniel, Stewart, Charles, Berger-Wolf, Tanya, Su, Yu, Chao, Wei-Lun
We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate class information to make predictions, we investigate a proactive approac
Externí odkaz:
http://arxiv.org/abs/2311.04157
Autor:
Tu, Cheng-Hao, Chen, Hong-You, Mai, Zheda, Zhong, Jike, Pahuja, Vardaan, Berger-Wolf, Tanya, Gao, Song, Stewart, Charles, Su, Yu, Chao, Wei-Lun
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data that covers only a partial label space. This problem is practical, as it
Externí odkaz:
http://arxiv.org/abs/2311.01420