  In this study, we address the challenging task of bridging the modality gap
between learning from language and inference for visual tasks, including Visual
Question Answering (VQA), Image Captioning (IC) and Visual Entailment (VE). We
train models for these tasks in a zero-shot cross-modal transfer setting, a
domain where the previous state-of-the-art method relied on the fixed scale
noise injection, often compromising the semantic content of the original
modality embedding. To combat it, we propose a novel method called Adaptive
ranged cosine Similarity injected noise (ArcSin). First, we introduce an
innovative adaptive noise scale that effectively generates the textual elements
with more variability while preserving the original text feature's integrity.
Second, a similarity pool strategy is employed, expanding the domain
generalization potential by broadening the overall noise scale. This dual
strategy effectively widens the scope of the original domain while safeguarding
content integrity. Our empirical results demonstrate that these models closely
rival those trained on images in terms of performance. Specifically, our method
exhibits substantial improvements over the previous state-of-the-art, achieving
gains of 1.9 and 1.1 CIDEr points in S-Cap and M-Cap, respectively.
Additionally, we observe increases of 1.5 percentage points (pp), 1.4 pp, and
1.4 pp in accuracy for VQA, VQA-E, and VE, respectively, pushing the boundaries
of what is achievable within the constraints of image-trained model benchmarks.
The code will be released.
