Short video uploading service has become increasingly important, as at least 30 million videos are uploaded per day. However, we find that existing congestion control (CC) algorithms, either heuristics or learning-based, are not applicable for video uploading -- i.e., lacking in the design of the fundamental mechanism and being short of leveraging network modeling. We present DuGu, a novel learning-based CC algorithm designed by considering the unique proprieties of video uploading via the probing phase and internet networking via the control phase. During the probing phase, DuGu leverages the transmission gap of uploading short videos to actively detect the network metrics to better understand network dynamics. DuGu uses a neural network~(NN) to avoid congestion during the control phase. Here, instead of using handcrafted reward functions, the NN is learned by imitating the expert policy given by the optimal solver, improving both performance and learning efficiency. To build this system, we construct an omniscient-like network emulator, implement an optimal solver and collect a large corpus of real-world network traces to learn expert strategies. Trace-driven and real-world A/B tests reveal that DuGu supports multi-objective and rivals or outperforms existing CC algorithms across all considered scenarios.