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Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition. We propose PatchNet, an efficient convolutional neural network to match objects in adjacent video frames. It learns the patchwise correlation features instead of pixel features. PatchNet is very compact, running at just 58MFLOPs, 5× simpler than MobileNetV2. We demonstrate its application on two tasks, video objectarXiv:2103.07371v1 fatcat:ejzywqowrjexfcrpibje7vxsvm