  Addressing multi-label action recognition in videos represents a significant
challenge for robotic applications in dynamic environments, especially when the
robot is required to cooperate with humans in tasks that involve objects.
Existing methods still struggle to recognize unseen actions or require
extensive training data. To overcome these problems, we propose Dual-VCLIP, a
unified approach for zero-shot multi-label action recognition. Dual-VCLIP
enhances VCLIP, a zero-shot action recognition method, with the DualCoOp method
for multi-label image classification. The strength of our method is that at
training time it only learns two prompts, and it is therefore much simpler than
other methods. We validate our method on the Charades dataset that includes a
majority of object-based actions, demonstrating that -- despite its simplicity
-- our method performs favorably with respect to existing methods on the
complete dataset, and promising performance when tested on unseen actions. Our
contribution emphasizes the impact of verb-object class-splits during robots'
training for new cooperative tasks, highlighting the influence on the
performance and giving insights into mitigating biases.
