One-shot Network Pruning at Initialization with Discriminative Image Patches

One-shot Network Pruning at Initialization (OPaI) is an effective method to decrease network pruning costs. Recently, there is a growing belief that data is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation experiments in two representative OPaI methods, SNIP and GraSP. Specifically, we find that informative data is crucial to enhancing pruning performance. In this paper, we propose two novel methods, Discriminative One-shot Network Pruning (DOP) and Super Stitching, to prune the network by high-level visual discriminative image patches. Our contributions are as follows. (1) Extensive experiments reveal that OPaI is data-dependent. (2) Super Stitching performs significantly better than the original OPaI method on benchmark ImageNet, especially in a highly compressed model.

WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

In this work, we contribute a new million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark, called WebUAV-3M. Firstly, we collect 4,485 videos with more than 3M frames from the Internet. Then, an efficient and scalable Semi-Automatic Target Annotation (SATA) pipeline is devised to label the tremendous WebUAV-3M in every frame. To the best of our knowledge, the densely bounding box annotated WebUAV-3M is by far the largest public UAV tracking benchmark. We expect to pave the way for the follow-up study in the UAV tracking by establishing a million-scale annotated benchmark covering a wide range of target categories. Moreover, considering the close connections among visual appearance, natural language and audio, we enrich WebUAV-3M by providing natural language specification and audio description, encouraging the exploration of natural language features and audio cues for UAV tracking. Equipped with this benchmark, we delve into million-scale deep UAV tracking problems, aiming to provide the community with a dedicated large-scale benchmark for training deep UAV trackers and evaluating UAV tracking approaches. Extensive experiments on WebUAV-3M demonstrate that there is still a big room for robust deep UAV tracking improvements. The dataset, toolkits and baseline results will be available at \url{https://github.com/983632847/WebUAV-3M}.