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A new completeness estimation approach is introduced that quantifies the degree of sentiment-relevant information preserved in incomplete data to guide the reconstruction of missing semantics and enables more accurate semantic reconstruction, leading to more precise sentiment prediction.
The proposed Multi-turn Multimodal Interactive Dialogue (MMID) Benchmark enables comprehensive evaluation of the Perception, Memorization, and Reasoning abilities of MLLMs, and reveals MLLMs perform well with text-based input but degrade with images, requiring improved leverage fine-grained visual cues.
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in multimodal understanding and reasoning by integrating linguistic and visual information, while benchmarks facilitate iterative model improvement by evaluating their performance and analyzing their limitations. However, existing dialogue-based...
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