Zobrazeno 1 - 10
of 3 006
pro vyhledávání: '"Beery, A."'
Autor:
Vendrow, Edward, Pantazis, Omiros, Shepard, Alexander, Brostow, Gabriel, Jones, Kate E., Mac Aodha, Oisin, Beery, Sara, Van Horn, Grant
We introduce INQUIRE, a text-to-image retrieval benchmark designed to challenge multimodal vision-language models on expert-level queries. INQUIRE includes iNaturalist 2024 (iNat24), a new dataset of five million natural world images, along with 250
Externí odkaz:
http://arxiv.org/abs/2411.02537
Autor:
Lee, Jae Joong, Li, Bosheng, Beery, Sara, Huang, Jonathan, Fei, Songlin, Yeh, Raymond A., Benes, Bedrich
We introduce Tree D-fusion, featuring the first collection of 600,000 environmentally aware, 3D simulation-ready tree models generated through Diffusion priors. Each reconstructed 3D tree model corresponds to an image from Google's Auto Arborist Data
Externí odkaz:
http://arxiv.org/abs/2407.10330
Image retrieval plays a pivotal role in applications from wildlife conservation to healthcare, for finding individual animals or relevant images to aid diagnosis. Although deep learning techniques for image retrieval have advanced significantly, thei
Externí odkaz:
http://arxiv.org/abs/2407.08908
Hierarchical semantic classification requires the prediction of a taxonomy tree instead of a single flat level of the tree, where both accuracies at individual levels and consistency across levels matter. We can train classifiers for individual level
Externí odkaz:
http://arxiv.org/abs/2406.11608
Autor:
Rolnick, David, Aspuru-Guzik, Alan, Beery, Sara, Dilkina, Bistra, Donti, Priya L., Ghassemi, Marzyeh, Kerner, Hannah, Monteleoni, Claire, Rolf, Esther, Tambe, Milind, White, Adam
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also
Externí odkaz:
http://arxiv.org/abs/2403.17381
Autor:
Kay, Justin, Haucke, Timm, Stathatos, Suzanne, Deng, Siqi, Young, Erik, Perona, Pietro, Beery, Sara, Van Horn, Grant
Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarki
Externí odkaz:
http://arxiv.org/abs/2403.12029
Autor:
Xu, Lily, Rolf, Esther, Beery, Sara, Bennett, Joseph R., Berger-Wolf, Tanya, Birch, Tanya, Bondi-Kelly, Elizabeth, Brashares, Justin, Chapman, Melissa, Corso, Anthony, Davies, Andrew, Garg, Nikhil, Gaylard, Angela, Heilmayr, Robert, Kerner, Hannah, Klemmer, Konstantin, Kumar, Vipin, Mackey, Lester, Monteleoni, Claire, Moorcroft, Paul, Palmer, Jonathan, Perrault, Andrew, Thau, David, Tambe, Milind
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-2
Externí odkaz:
http://arxiv.org/abs/2307.08774
Autor:
Chen, Jun, Hu, Ming, Coker, Darren J., Berumen, Michael L., Costelloe, Blair, Beery, Sara, Rohrbach, Anna, Elhoseiny, Mohamed
Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of animals and their behaviors is critical for capitalizing on the large u
Externí odkaz:
http://arxiv.org/abs/2306.00576
Autor:
Kuznedelev, Denis, Tabesh, Soroush, Noorbakhsh, Kimia, Frantar, Elias, Beery, Sara, Kurtic, Eldar, Alistarh, Dan
Recent vision architectures and self-supervised training methods enable vision models that are extremely accurate and general, but come with massive parameter and computational costs. In practical settings, such as camera traps, users have limited re
Externí odkaz:
http://arxiv.org/abs/2303.14409
Autor:
Cole, Elijah, Stathatos, Suzanne, Lütjens, Björn, Sharma, Tarun, Kay, Justin, Parham, Jason, Kellenberger, Benjamin, Beery, Sara
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites. However, computer vision is an emerging discipline that is rarely taught to ecologists. This work discus
Externí odkaz:
http://arxiv.org/abs/2301.02211