VirTex: Learning Visual Representationsfrom Textual Annotations
Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3).
['[BOLD] Visual Backbone', '[BOLD] VOC07', '[BOLD] IN-1k']
Do bigger visual backbones help? We investigate whether scaling backbone capacity in VirTex models can improve performance on downstream tasks. We train two VirTex models with bigger visual backbones and compare them with base model using ResNet-50 – (a) ResNet-50 w2× (2× channel width), and (b) ResNet-101 ( 2× depth). Both variants use textual head with L=1,H=1024. Using higher-capacity backbones improve downstream performance on both PASCAL VOC and ImageNet-1k.