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pro vyhledávání: '"Tarr, Michael J."'
Autor:
Luo, Andrew F., Yeung, Jacob, Zawar, Rushikesh, Dewan, Shaurya, Henderson, Margaret M., Wehbe, Leila, Tarr, Michael J.
Advances in large-scale artificial neural networks have facilitated novel insights into the functional topology of the brain. Here, we leverage this approach to study how semantic categories are organized in the human visual cortex. To overcome the c
Externí odkaz:
http://arxiv.org/abs/2410.05266
Autor:
Sarch, Gabriel, Jang, Lawrence, Tarr, Michael J., Cohen, William W., Marino, Kenneth, Fragkiadaki, Katerina
Large-scale generative language and vision-language models (LLMs and VLMs) excel in few-shot in-context learning for decision making and instruction following. However, they require high-quality exemplar demonstrations in their context window. In thi
Externí odkaz:
http://arxiv.org/abs/2406.14596
Autor:
Zawar, Rushikesh, Dewan, Shaurya, Luo, Andrew F., Henderson, Margaret M., Tarr, Michael J., Wehbe, Leila
Understanding the semantics of visual scenes is a fundamental challenge in Computer Vision. A key aspect of this challenge is that objects sharing similar semantic meanings or functions can exhibit striking visual differences, making accurate identif
Externí odkaz:
http://arxiv.org/abs/2406.13735
Autor:
Yeung, Jacob, Luo, Andrew F., Sarch, Gabriel, Henderson, Margaret M., Ramanan, Deva, Tarr, Michael J.
While computer vision models have made incredible strides in static image recognition, they still do not match human performance in tasks that require the understanding of complex, dynamic motion. This is notably true for real-world scenarios where e
Externí odkaz:
http://arxiv.org/abs/2406.02659
Recent research on instructable agents has used memory-augmented Large Language Models (LLMs) as task planners, a technique that retrieves language-program examples relevant to the input instruction and uses them as in-context examples in the LLM pro
Externí odkaz:
http://arxiv.org/abs/2404.19065
Do machines and humans process language in similar ways? Recent research has hinted in the affirmative, finding that brain signals can be effectively predicted using the internal representations of language models (LMs). Although such results are tho
Externí odkaz:
http://arxiv.org/abs/2311.09308
Pre-trained and frozen large language models (LLMs) can effectively map simple scene rearrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting. To parse open-domain natural language and
Externí odkaz:
http://arxiv.org/abs/2310.15127
Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias
Externí odkaz:
http://arxiv.org/abs/2310.04420
Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation, composition, collection process, etc..."). However, almost no one has
Externí odkaz:
http://arxiv.org/abs/2306.14035
A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selec
Externí odkaz:
http://arxiv.org/abs/2306.03089