A Fast and Accurate One-Stage Approach to Visual Grounding

We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight. The performances of existing propose-and-rank two-stage methods are capped by the quality of the region candidates they propose in the first stage --- if none of the candidates could cover the ground truth region, there is no hope in the second stage to rank the right region to the top... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Logistic Regression
Generalized Linear Models
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
k-Means Clustering
Clustering
Softmax
Output Functions
Residual Connection
Skip Connections
Convolution
Convolutions
Darknet-53
Convolutional Neural Networks
YOLOv3
Object Detection Models