Continual and Multi-Task Architecture Search
Table 3: Video captioning results with Baseline, ENAS, and CAS models. Baseline is reproduced numbers from github of pasunuru2017reinforced which uses advanced latest visual features (ResNet-152 and ResNeXt-101) for video encoder. C, B, R, M: CIDEr, BLEU-4, ROUGE-L, and METEOR metrics.
['Models', 'MSR-VTT C', 'MSR-VTT B', 'MSR-VTT R', 'MSR-VTT M', 'MSR-VTT AVG', 'MSVD C', 'MSVD B', 'MSVD R', 'MSVD M', 'MSVD AVG']
Baselines Models: Our baseline is a sequence-to-sequence model with attention mechanism as described in Sec. We achieve comparable results w.r.t. SotA ENAS Models: Next, we apply our continual architecture search (CAS) We observe that even though we learn the models sequentially, we are able to maintain performance on the previously-learned MSR-VTT task in step-2, while also achieving greater-or-equal performance on the current task of MSVD in comparison with the general ENAS approach.