实验结果表明,所提出的端到端的FAST-VQA和FasterVQA在所有VQA基准测试上的性能显著优于现有方法,同时计算效率提高了1612倍。这使得深度VQA算法可以应用于任何分辨率的视频,无论视频长度如何。 模型介绍 框图 邻域采样表征(Sampling Representatives from Neighbourhoods)在视觉任务中,采样的应用非常广泛。具体而言,均匀采样...
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome - peteanderson80/bottom-up-attention
We focus on performance in downstream tasks (e.g. image captioning, VQA) rather than detection performance.About Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome panderson.me/up-down-attention/ Resources Readme License MIT license Activity ...
Updated cudnn, added visual genome datase and resnet-101 faster rcnn model 8年前 experiments Add example of log file from initial training with a single gpu 7年前 lib Updated cudnn, added visual genome datase and resnet-101 faster rcnn model ...
利用片段和FANet,所提出的效率端到端的FAST-VQA和FasterVQA在所有VQA基准测试上的性能显著优于现有方法,同时仅需要1/1612的FLOPs,相较于当前最先进技术大幅降低。介绍随着高清拍摄设备的普及和视频压缩等技术的进步,大多数用户拍摄的视频分辨率大大提高,例如1080P、4K,甚至是8K,这极大地丰富了人类的感知和娱乐方式。
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome - jiasenlu/bottom-up-attention
We focus on performance in downstream tasks (e.g. image captioning, VQA) rather than detection performance.About Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome panderson.me/up-down-attention/ Resources Readme License MIT license Activity ...