So here's what's funny about the leaderboard. The NeuralTalk submission (near bottom of the list) is in fact a re-implementation of the Show and Tell model (my paper and Oriol's are basically identical models - we plug in the top of the CNN to an RNN on first time step. Except I use vanilla RNN and he used an LSTM (which turns out to work a bit better)). But it's an identical model - in that submission I had in fact used the LSTM implementation in NeuralTalk.

What's different between the two is the engineering portion: NeuralTalk was written in Python and ran on CPU without batches (so probably it did not converge as well), it did not do CNN finetuning (which helps a ton), and it did not do ensembles, and I used VGG while Oriol used GoogLeNet (though I don't expect much difference there). There are a few more tricks that Oriol talks about that give you a few small extra points, but that's basically it. I also don't want to take away from Oriol's top result (these small tricks and engineering are very valuable and hard to come up with), but I would be hesitant to draw conclusions about which models work best by looking at the model diagrams in a Figure, and comparing to numbers in the table.

That's why I've over time grown cynical about results in tables of papers that compare one work to another - there are too many variables to keep track of and it's confusing unless you know the full details. The truth is that results in tables are model + engineering + noise. The papers pretend that it's all model, but in fact the latter 2 have a huge impact. At least results that only compare two models in the same framework can be trusted a bit more to contain information, but even then you find that in practice people can be a lot more generous to their own models than their baselines. Hence the famous saying: "The second best model in the paper is in fact what you want to use".

To answer the original question though: The idea presented in Show Attend and Tell (spatial attention during caption generation) is clearly good and if compared properly I'm confident would turn out to work better. Also, the Berkeley paper seemed to have a nice architecture where there was a 2-layer LSTM but the image was only plugged in on the second layer (1st layer was an image-independent Language Model). In their controlled experiments this seemed to work well, I think it's an interesting idea, and I'd want to try to reproduce it. The models presented in my work and Oriol's are the simplest architecture that has the core nugget of the approach and that gets the job done, but I'd expect many bells and whistles on top of this to work better.