What is the minimum/suggested sequence length for training an LSTM? My dataset consists of short videos of 4/5 time-steps each (frames), and the problem is classifying this video (multi-label classification). The idea is to use an LSTM but I'm wondering if the sequence length is not enough.
 What are the suggested sequence length? May 4/5 time steps be enough?

P.S. could you please post some link to scientific articles to endorse your claims?
LSTMs would run into problems beyond 500 time steps. 
original paper by Hochreiter and Schmidhuber: "LSTM can learn to bridge minimal time lags in excess of 1000 discrete time steps."

Example: Your video is 60 seconds long. I would start by encoding 5 frames per second using a CNN (use resnet (SEnets have better performance but i havent read the paper) till the layer just before final affine layer which feeds into the softmax encode your images. Use these encodings as inputs in a GRU (Hence a 300 time steps for this GRU) and use its final state to make the class prediction (use a separate loss for each label?)