In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.

LiDAR-based 3D object detection has been widely utilized in fields such as autonomous driving and robotics. However, the black-box nature of 3D models limits their interpretability, making it difficult to understand their predictions and evaluate significant feature contributions, which are essential for safety-critical applications. To address this, we propose a novel knowledge distillation method for 3D object detection that integrates explanation-based and aggregation techniques to achieve effective knowledge transfer and, as a result, enhance the model’s interpretability. Our method generates attribution maps that highlight the importance of 3D points in the teacher model and aggregates them into a single map. This map is aligned with the student model’s pillar features using corresponding coordinates, allowing pillar-wise feature mapping. Building on this feature mapping, to the best of our knowledge, this is the first study to propose a distillation process that effectively transfers the teacher model’s explanations of critical regions to the student model. Experimental results demonstrate that the proposed method increases 3D and BEV mAP by up to 2.09% and 0.84%, respectively, compared to the existing models.