Transformer-based text-image matching model, known as CLIP, has garnered significant attention owing to its exceptional performance in text-image retrieval tasks and downstream applications. However, the interpret-ability of CLIP remains underexplored. Existing interpretation methods for Transformers often struggle with incomplete and unreliable attributions within the image and text modalities, respectively. In this paper, we propose a fine-grained interpretation method, termed Class Activation Values (CAV), to provide lucid and faithful visual explanations for CLIP-based text-image retrievals. Specifically, we systematically perform multi-scale accumulation and fusion of class-specific gradients and activation value features to generate high-definition explanations for the image encoder. Furthermore, we present element-wise gradient-based weights to attribute fine-grained relevance between value features and output similarity within the text encoder. The proposed CAV is capable of simultaneously rendering detailed and credible explanations due to its precise feature attribution. Extensive qualitative and quantitative experiments are conducted on the ImageNet-1k and MS COCO datasets, and the experimental results demonstrate that CAV outperforms state-of-the-art interpretation methods in both faithfulness and localization assessments across image and text modalities.

The development of Wireless Power Transfer (WPT) technology presents a promising avenue to support rechargeable UAVs in executing a wide range of energy-intensive tasks. However, most UAV path-planning schemes focus solely on task completion and maximizing coverage, often overlooking how UAVs should navigate in the presence of multiple charging stations organized as a wireless charging network that can replenish their power. This paper studies the problem of determining the service points serving the ground users as well as the trajectory of the wireless-powered UAV in the wireless charging network to maximize the total user-UAV communication utility. To this end, we formulate an optimization problem under typical path constraints, which introduces significant complexity. We propose a novel algorithm, where an improved k-means++ algorithm is employed to determine potential service points for the UAV, and the optimized trajectory is generated at each step by comparing the quantified communication utility and energy consumption utility. The performance of the proposed algorithm is evaluated through simulation experiments. Our results demonstrate 8.68% ~ 158.74% improvement compared with comparison algorithms.

Active Speaker Detection (ASD) plays a crucial role in scene understanding tasks by determining whether an on-screen person in a given scene is speaking.In this work, to address the ASD in the context of multi-party roundtable meetings, we propose a novel approach that incorporates the fusion of spatial information of the scenes.To leverage the multiple data sources of the scenes, our method involves generating audio spatial spectrum heatmaps from the multi-channel audio and integrating them with the panoramic images.Additionally, we propose the novel FisheyeMeeting dataset, which combines fisheye panoramic video recordings with muti-channel audio captured from a six-channel circular microphone array.By enabling the multi-modal model to capture audio-visual cues in multi-party meeting scenes, our approach achieves an impressive 89.11% mAP on the FisheyeMeeting dataset.Notably, this outperforms the current SOTA methods by a significant 2.3% mAP improvement.