Auto-captions on GIF: A Large-scale Video-sentence Dataset forVision-language Pre-training
Table 2: Performance comparisons on MSR-VTT with official split, where B@4, M, R, C and S are short for BLEU@4, METEOR, ROUGE-L, CIDEr-D and SPICE scores. All values are reported as percentage (%). The short name in the brackets indicates the frame/clip features, where G, C, R, I and A denotes GoogleNet, C3D, ResNet, Inception-Resnet-V2 and Audio feature.
['Model', 'B@4', 'M', 'R', 'C', 'S']
Offline Evaluation on Official Split. It is worth noting that the reported performances of different state-of-the-art task-specific models are often based on different frame/clip representations. For fair comparisons, we evaluate our base models ( TransED, TransEDRL) on the most commonly adopted frame representation (i.e., the output from ResNet). Moreover, we involve two different experimental settings for each base model: TransED/TransEDRL denotes the base model which is only trained with task-specific data, without pre-training on our Auto-captions on GIF dataset ; TransED/TransEDRL+Pre-training represents that the base model is pre-trained over Auto-captions on GIF and further fine-tuned on task-specific data.