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2209.06293] Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest Skip to

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1810.09160] Who Filters the Filters: Understanding the Growth, Usefulness and Efficiency of Crowdsourced

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MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding Skip to main

to lack spatial awareness and have limited direct applicability for dense understanding tasks. For this

world and makes rapid progress. However, limited by existing datasets, previous works mainly focus on understanding object properties or inter

brief description, a process that intertwines image interpretation, world knowledge, and an understanding of body

transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross

keeping the LLM's parameters frozen while integrating vision-specific information for both understanding and generation

scenarios. These text-based games are challenging for artificial agents, as it requires an understanding of and

22 other authors View PDF HTML (experimental) Abstract: The systematic evaluation and understanding of computer

in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to

View PDF Abstract: Grouping and recognition are important components of visual scene understanding, e.g., for object

models and use them for tasks that require a fine-grained understanding of how

Chen and 7 other authors View PDF HTML (experimental) Abstract: Prior studies on 3D scene understanding have primarily developed specialized

than 3 million instances, outperforms similar-size models trained on over 300 million instances on relationship understanding benchmarks, and even

but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM

transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross

Mapping the local and distant Universe is key to our understanding of it

last few years. Despite impressive successes on downstream tasks, we still have a limited understanding of the

it can be fine-tuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering

capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a