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pro vyhledávání: '"TAYLOR, Graham"'
We present Agglomerative Token Clustering (ATC), a novel token merging method that consistently outperforms previous token merging and pruning methods across image classification, image synthesis, and object detection & segmentation tasks. ATC merges
Externí odkaz:
http://arxiv.org/abs/2409.11923
Autor:
Gupta, Akshita, Arora, Aditya, Narayan, Sanath, Khan, Salman, Khan, Fahad Shahbaz, Taylor, Graham W.
Open-Vocabulary Temporal Action Localization (OVTAL) enables a model to recognize any desired action category in videos without the need to explicitly curate training data for all categories. However, this flexibility poses significant challenges, as
Externí odkaz:
http://arxiv.org/abs/2406.15556
Autor:
Gharaee, Zahra, Lowe, Scott C., Gong, ZeMing, Arias, Pablo Millan, Pellegrino, Nicholas, Wang, Austin T., Haurum, Joakim Bruslund, Zarubiieva, Iuliia, Kari, Lila, Steinke, Dirk, Taylor, Graham W., Fieguth, Paul, Chang, Angel X.
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establish several benchmark tasks. BIOSCAN-5M is a comprehensive dataset con
Externí odkaz:
http://arxiv.org/abs/2406.12723
Autor:
Lowe, Scott C., Haurum, Joakim Bruslund, Oore, Sageev, Moeslund, Thomas B., Taylor, Graham W.
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments
Externí odkaz:
http://arxiv.org/abs/2406.02465
Conformal prediction (CP) enables machine learning models to output prediction sets with guaranteed coverage rate, assuming exchangeable data. Unfortunately, the exchangeability assumption is frequently violated due to distribution shifts in practice
Externí odkaz:
http://arxiv.org/abs/2406.01416
Autor:
Gong, ZeMing, Wang, Austin T., Huo, Xiaoliang, Haurum, Joakim Bruslund, Lowe, Scott C., Taylor, Graham W., Chang, Angel X.
Measuring biodiversity is crucial for understanding ecosystem health. While prior works have developed machine learning models for taxonomic classification of photographic images and DNA separately, in this work, we introduce a multimodal approach co
Externí odkaz:
http://arxiv.org/abs/2405.17537
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video backbones to outperform previous methods needing both RGB and optical
Externí odkaz:
http://arxiv.org/abs/2404.01282
Autor:
Sheraz, Haleema, Kremer, Stefan C., Skorburg, Joshua August, Taylor, Graham, Sinnott-Armstrong, Walter, Boerstler, Kyle
In response to the pressing challenge of kidney allocation, characterized by growing demands for organs, this research sets out to develop a data-driven solution to this problem, which also incorporates stakeholder values. The primary objective of th
Externí odkaz:
http://arxiv.org/abs/2401.15268
Autor:
Arias, Pablo Millan, Sadjadi, Niousha, Safari, Monireh, Gong, ZeMing, Wang, Austin T., Lowe, Scott C., Haurum, Joakim Bruslund, Zarubiieva, Iuliia, Steinke, Dirk, Kari, Lila, Chang, Angel X., Taylor, Graham W.
Understanding biodiversity is a global challenge, in which DNA barcodes - short snippets of DNA that cluster by species - play a pivotal role. In particular, invertebrates, a highly diverse and under-explored group, pose unique taxonomic complexities
Externí odkaz:
http://arxiv.org/abs/2311.02401