Despite their shortcomings (e.g., they often cover a small fraction of the vocabulary of an adult speaker due to the effort involved in eliciting them), feature norms provide detailed knowledge about meaning representations and are a useful starting point for studying the integration of perceptual and textual information without being susceptible to the effects of noise, e.g., coming from image processing.
feature norms can serve as an upper bound of what can be achieved when integrating detailed perceptual information with vanilla text-based distributional models.