The paper "Improved Baselines for Data-efficient Perceptual Augmentation of LLMs" by Théophane Vallayes, Mustafa Shukor, Matthieu Cord, and Jakob Verbeek presents a comprehensive study on the interfacing mechanisms between large language models (LLMs) and perceptual backbones for multimodal tasks. The authors address the challenge of adapting LLMs to tasks involving image, video, and audio data, focusing on both parameter efficiency and data efficiency. Key contributions of the paper include: 1. **Unified Framework**: A systematic framework for evaluating various interfacing mechanisms, including feature extraction, mapping, injection, and fine-tuning. 2. **Experimental Evaluation**: Extensive experiments across multiple tasks, datasets, and backbones, highlighting the impact of different design choices. 3. **New Mechanism**: Introduction of DePALM, a novel mechanism that achieves near-optimal results across different tasks while reducing training time by up to 4×. The paper identifies several key findings: - **Performance Improvements**: Existing mechanisms outperform previous state-of-the-art results, even with careful hyperparameter tuning. - **DePALM**: DePALM, a mechanism that aggregates perceptual tokens using a query pooling mapper (QPMapper), is identified as the most effective approach, achieving near-optimal results with significantly reduced training time. - **Efficiency and Performance Trade-offs**: The choice of perceptual encoders and LLMs significantly impacts performance and efficiency. Text-aligned perceptual encoders, such as CLIP, perform better than supervised or self-supervised encoders. - **Parameter and Data Efficiency**: The proposed methods scale well with limited training data, achieving high performance with only 1% of the training set. The paper concludes by discussing the complementary nature of large-scale and efficient setups, emphasizing the importance of both approaches for effective multimodal models. It also highlights the need for further research in safety and broader objectives, such as aligning models with human preferences.The paper "Improved Baselines for Data-efficient Perceptual Augmentation of LLMs" by Théophane Vallayes, Mustafa Shukor, Matthieu Cord, and Jakob Verbeek presents a comprehensive study on the interfacing mechanisms between large language models (LLMs) and perceptual backbones for multimodal tasks. The authors address the challenge of adapting LLMs to tasks involving image, video, and audio data, focusing on both parameter efficiency and data efficiency. Key contributions of the paper include: 1. **Unified Framework**: A systematic framework for evaluating various interfacing mechanisms, including feature extraction, mapping, injection, and fine-tuning. 2. **Experimental Evaluation**: Extensive experiments across multiple tasks, datasets, and backbones, highlighting the impact of different design choices. 3. **New Mechanism**: Introduction of DePALM, a novel mechanism that achieves near-optimal results across different tasks while reducing training time by up to 4×. The paper identifies several key findings: - **Performance Improvements**: Existing mechanisms outperform previous state-of-the-art results, even with careful hyperparameter tuning. - **DePALM**: DePALM, a mechanism that aggregates perceptual tokens using a query pooling mapper (QPMapper), is identified as the most effective approach, achieving near-optimal results with significantly reduced training time. - **Efficiency and Performance Trade-offs**: The choice of perceptual encoders and LLMs significantly impacts performance and efficiency. Text-aligned perceptual encoders, such as CLIP, perform better than supervised or self-supervised encoders. - **Parameter and Data Efficiency**: The proposed methods scale well with limited training data, achieving high performance with only 1% of the training set. The paper concludes by discussing the complementary nature of large-scale and efficient setups, emphasizing the importance of both approaches for effective multimodal models. It also highlights the need for further research in safety and broader objectives, such as aligning models with human preferences.