While the All-Seeing Project V2 presents significant advancements in relation comprehension for Multi-modal Large Language Models (MLLMs), there are some potential limitations or criticisms that could be considered:
Dataset Bias: The AS-V2 dataset used for training ASMv2 may have biases inherent in its construction process which could affect model performance on real-world data.
Generalization: The model's performance on specific benchmarks may not fully translate into real-world scenarios where diverse and unpredictable data is encountered.
Interpretability: As MLLMs become more complex with additional capabilities like relation conversation, interpretability of these models becomes challenging which might raise concerns about transparency and accountability.
Ethical Considerations: With powerful AI systems like ASMv2 advancing towards artificial general intelligence (AGI), ethical considerations around privacy, bias mitigation, and responsible AI development need careful attention.