Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.

The heads are then concatenated to form the LoRA adapter.

Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems.

In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models.

On the ViT -L/16 architecture, our experiments show that a single adapter, 1.3% of the full model, is able to reach full fine-tuning accuracy on average across 11 challenging downstream classification tasks. Compared with other forms of parameter-efficient adaptation, the isolated nature of the InCA adaptation is computationally desirable for large-scale models. For instance, we adapt ViT -G/14 (1.8B+ parameters) quickly with 20+ adapters in parallel on a single V100 GPU (76% GPU memory reduction) and exhaustively identify its