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  • MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION
    #REDIRECT [[mULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION]] ...
    63 bytes (7 words) - 09:46, 30 August 2017
  • Summary for Saliency-based Sequential Image Attention with Multiset Prediction
    #REDIRECT [[stat441w18/Saliency-based Sequential Image Attention with Multiset Prediction]] ...
    91 bytes (10 words) - 12:47, 15 March 2018
  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
    ...IRECT [[show, Attend and Tell: Neural Image Caption Generation with Visual Attention]] ...
    90 bytes (12 words) - 09:46, 30 August 2017
  • learning2reasoning
    ...ence and machine learning and machine reasoning have received considerable attention given the short history of computer science. The statistical nature of mach ...
    852 bytes (116 words) - 09:46, 30 August 2017
  • stat940F21
    ...Benyamin Jamialahmad || || Perceiver: General Perception with Iterative Attention || [https://arxiv.org/abs/2103.03206] ||[https://www.youtube.com/watch?v=N |Week of Nov 25 || Mina Kebriaee || || Synthesizer: Rethinking Self-Attention for Transformer Models ||[https://arxiv.org/pdf/2005.00743.pdf] || [https:/ ...
    5 KB (642 words) - 23:29, 1 December 2021
  • show, Attend and Tell: Neural Image Caption Generation with Visual Attention
    ...v2.pdf "Show, attend and tell: Neural image caption generation with visual attention."] arXiv preprint arXiv:1502.03044 (2015). </ref> introduces an attention based model that automatically learns to describe the content of images. It ...
    12 KB (1,882 words) - 09:46, 30 August 2017
  • mULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION
    ...pecific manner for determining possible object locations. In this paper an attention-based model for recognizing multiple objects in images is presented. The pr = Deep Recurrent Visual Attention Model:= ...
    11 KB (1,714 words) - 09:46, 30 August 2017
  • Do Vision Transformers See Like CNN
    ...LSTMs, …) were experiencing at the time by introducing the concept of self-attention. ...the sentence as keys. Self-attention effectively tells the model how much attention the query should give to other words in the sentence. ...
    13 KB (2,006 words) - 00:11, 17 November 2021
  • STAT946F20/BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
    ...ion and the sequential mask in the decoder and usually performs Multi-head attention to derive more features from the different subspace of sentence for the ind ...ture and the only difference is that <math>BERT_{BASE}</math> makes use of attention masks and gets and improvement of 4.5%. It can also be seen that <math>BERT ...
    9 KB (1,342 words) - 06:36, 10 December 2020
  • Hierarchical Question-Image Co-Attention for Visual Question Answering
    ...ur model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution Recently, ''visual-attention'' based models have gained traction for VQA tasks, where the ...
    27 KB (4,375 words) - 19:50, 28 November 2017
  • One-Shot Imitation Learning
    ...tance of the same task. Strong generalization was achieved by using a soft attention mechanism on both the sequence of actions and states that the demonstration * Attention Modelling: ...
    20 KB (3,247 words) - 00:27, 21 April 2018
  • f15Stat946PaperSignUp
    ...per]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]] ....org/pdf/1412.7755v2.pdf Paper]||[[MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION | Summary]] ...
    11 KB (1,453 words) - 13:01, 16 October 2018
  • Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
    ...1708.00339 "Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin."] by Singh, Ritambhara, et al. It was published at the Advanc ...o attend to the important marks and their interactions. In this context, ''attention'' refers to weighing the importance of different items differently. ...
    33 KB (4,924 words) - 20:52, 10 December 2018
  • DCN plus: Mixed Objective And Deep Residual Coattention for Question Answering
    ...g: Models like coattention, bidirectional attention flow and self-matching attention build codependent representations of the question and the document. After b ...odels Intuition 2]. Coattention, bidirectional attention and self-matching attention are some of the methods that build codependent representation between the q ...
    24 KB (3,769 words) - 17:49, 14 December 2018
  • Convolutional Sequence to Sequence Learning
    ...ast models employ RNNs for this problem, with bidirectional RNNs with soft attention being the dominant approach. ...ases gradient propagation and equipping each decoder layer with a separate attention module adds a negligible amount of overhead. ...
    27 KB (4,178 words) - 20:37, 28 November 2017
  • ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
    .... The authors set the feed-forward/filter size to be 4*H and the number of attention heads to be H/64 (where H is the size of the hidden layer). Next, we explai ...example, one may only share feed-forward network parameters or only share attention parameters. However, the default choice for ALBERT is to simply share all p ...
    14 KB (2,170 words) - 21:39, 9 December 2020
  • stat441w18
    || 9|| Saliency-based Sequential Image Attention with Multiset Prediction ...
    5 KB (694 words) - 18:02, 31 August 2018
  • One-Shot Object Detection with Co-Attention and Co-Excitation
    ...t image based on the query image. This operation can be thought of as a co-attention mechanism. The second contribution is proposing a Squeeze and Co-Excitation ...e. The same can be observed for the query image. This weighted sum is a co-attention mechanism and with the help of extended feature maps, better proposals are ...
    22 KB (3,609 words) - 21:53, 6 December 2020
  • This Looks Like That: Deep Learning for Interpretable Image Recognition
    ...ing classification but are rather created after this phase. There are also attention-based models that determine parts of the input they are looking at but with ...s as well as the car models are compared to the baseline models as well as attention-based deep models that were trained on the same datasets that ProtoPNet was ...
    10 KB (1,573 words) - 23:36, 9 December 2020
  • f17Stat946PaperSignUp
    |Nov 28 || Shivam Kalra ||29 || Hierarchical Question-Image Co-Attention for Visual Question Answering || [https://arxiv.org/pdf/1606.00061.pdf Pape ...
    10 KB (1,213 words) - 19:28, 19 November 2020
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