The field of vision and language models is moving towards more efficient adaptation and inference methods. Recent developments focus on reducing computational overhead and latency while maintaining or improving performance. This is achieved through novel token aggregation methods, split-and-share multi-modal architectures, and efficient inter-task attention mechanisms. Noteworthy papers include:

• Neutralizing Token Aggregation via Information Augmentation for Efficient Test-Time Adaptation, which proposes a method to reduce inference latency while preserving adaptation capability.
• S2M3: Split-and-Share Multi-Modal Models for Distributed Multi-Task Inference on the Edge, which introduces a split-and-share architecture to reduce resource usage and inference latency.
• Efficient Inter-Task Attention for Multitask Transformer Models, which proposes a novel attention mechanism to reduce computational overhead.
• FrEVL: Leveraging Frozen Pretrained Embeddings for Efficient Vision-Language Understanding, which explores the use of frozen pretrained embeddings for efficient vision-language understanding.
• REINA: Regularized Entropy Information-Based Loss for Efficient Simultaneous Speech Translation, which presents a novel loss function to optimize the tradeoff between translation quality and latency.