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Status: Unchecked ×Keyword: image-text pairs ×Clear all

5 claims from 2 papers

1. Computer Science › Human Pose and Action Recognition

   Microsoft COCO: Common Objects in Context

   Tsung-Yi, Michael, Serge et al. · arXiv (Cornell University) · 2014

   The paper presents COCO, a dataset of everyday scenes with labelled common objects, built with crowd workers and compared with PASCAL, ImageNet and SUN, with baseline detection results.

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   1. UncheckedThe COCO dataset contains photos of 91 object types that the authors say a 4-year-old would easily recognise.“Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old.”
   2. UncheckedThe COCO dataset holds 2.5 million labelled instances in 328k images, built with heavy crowd-worker input through new interfaces for labelling.“With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation.”
   3. UncheckedIn the COCO dataset, each object is outlined individually with a segmentation, which the authors say helps pinpoint where objects are in an image.“Objects are labeled using per-instance segmentations to aid in precise object localization.”

2. Computer Science › Multimodal Machine Learning Applications

   VILA: On Pre-training for Visual Language Models

   Ji, Yin, Ping, Molchanov, Shoeybi and Han · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

   The paper studies how to pre-train visual language models, reports three findings on design choices, and uses them to build VILA, a model family that also shows extra abilities and runs on-device.

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   1. UncheckedThe authors say VILA, built with an improved pre-training recipe, consistently beats state-of-the-art models such as LLaVA-1.5 on main benchmarks.“With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles.”
   2. UncheckedThe paper says multi-modal pre-training brings out useful abilities in its VILA models: multi-image reasoning, stronger in-context learning and better world knowledge.“Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.”