R-AVST: Empowering Video-LLMs with Fine-Grained Spatio-Temporal Reasoning in Complex Audio-Visual Scenarios

A new dataset named Q-Real has been introduced to evaluate the realism and plausibility of AI-generated images, consisting of 3,088 images annotated for major entities and judgment questions. This initiative aims to enhance the quality assessment of generative models, moving beyond the limitations of existing datasets that provide only a single quality score.

SpatialGeo has been introduced as a novel vision encoder that enhances the spatial reasoning capabilities of multimodal large language models (MLLMs) by integrating geometry and semantics features. This advancement addresses the limitations of existing MLLMs, particularly in interpreting spatial arrangements in three-dimensional space, which has been a significant challenge in the field.

A new approach to multimodal KV Cache compression has been proposed, focusing on the distribution of KV matrices' energy in the frequency domain. This method identifies and removes outlier KV pairs that deviate from the principal energy, which significantly impacts the performance of multimodal large language models (MLLMs). The study highlights the limitations of existing compression methods that rely solely on attention scores.