On the Language Encoder of Contrastive Cross-modal Models
This work addresses a key bottleneck in cross-modal AI systems for researchers and practitioners, though it is incremental as it builds on existing models.
The paper tackled the problem of improving language encoders in contrastive cross-modal models like CLIP and CLAP, finding that sentence embedding training enhances language encoder quality and boosts performance in vision-language tasks, such as improving CyCLIP, but offers limited benefits in audio-language tasks due to data constraints.
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks. However, there has been limited investigation of and improvement in their language encoder, which is the central component of encoding natural language descriptions of image/audio into vector representations. We extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance. In VL pretraining, we found that sentence embedding training language encoder quality and aids in cross-modal tasks, improving contrastive VL models such as CyCLIP. In contrast, AL pretraining benefits less from sentence embedding training, which may result from the limited amount of pretraining data. We analyze the representation spaces to understand the strengths of sentence embedding training, and find that it improves text-space uniformity, at the cost of decreased cross-modal alignment.