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Found 5 papers, 5 papers with code
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module.
Glance and Focus Networks for Dynamic Visual Recognition
Spatial redundancy widely exists in visual recognition tasks, i. e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand.
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
Adaptive Focus for Efficient Video Recognition
In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency.
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency.
