Archives for self attention models - Page 2
VIBE - Video Inference for 3D Human Body Pose and Shape Estimation. It uses CNNs, RNNs(GRU) and GANs along with a self-attention layer to achieve its state-of-the-art results.
The post Guide To VIBE: Video Inference for 3D Human Body Pose and Shape Estimation appeared first on Analytics India Magazine.
ViT breaks an input image of 16x16 to a sequence of patches, just like a series of word embeddings generated by an NLP Transformers. Each patch gets flattened into a single vector in a series of interconnected channels of all pixels in a patch, then projects it to desired input dimension.
The post Hands-on Vision Transformers with PyTorch appeared first on Analytics India Magazine.
The key component of the transformer architecture is the attention module. Its job is to figure out the matching pairs (think: Translation) in a sequence through similarity scores. When the length of a sequence increases, calculating similarity scores for all pairs gets inefficient. So, the researchers have come up with the sparse attention technique where…
The post Thinking Beyond Transformers: Google Introduces Performers appeared first on Analytics India Magazine.
The key component of the transformer architecture is the attention module. Its job is to figure out the matching pairs (think: Translation) in a sequence through similarity scores. When the length of a sequence increases, calculating similarity scores for all pairs gets inefficient. So, the researchers have come up with the sparse attention technique where…
The post Thinking Beyond Transformers: Google Introduces Performers appeared first on Analytics India Magazine.
It might have never occurred to you how you could make sense of what your friend is blabbering at a loud party. There are all kinds of noises in a party; then how come we are perfectly able to carry out a conversation? This question is known widely as the ‘cocktail party problem’. Most of…
The post A Beginner’s Guide To Attention And Memory In Deep Learning appeared first on Analytics India Magazine.
The advent of large datasets and compute resources made convolution neural networks (CNNs) the backbone for many computer vision applications. The field of deep learning has in turn largely shifted toward the design of architectures of CNNs for improving the performance on image recognition. Poor scaling properties in convolutional neural networks (CNNs) make capturing…
The post How To Go Beyond CNNs With Stand-Alone Self-Attention Models appeared first on Analytics India Magazine.