Deep multi-view clustering (MVC) has gained widespread attention as it can effectively mine consistent information from multiple views and improve clustering performance. However, view bias often exists between views (i.e., the quality differences between views). Treating all views equally inevitably destroys structural information when simply concatenating or summing the embedded representation of multiple views. To alleviate this issue, we propose a deep multi-view clustering with intra-view similarity and cross-view correlation learning (MISCC), facilitating the intra-view discriminability and inter-view complementarity. Specifically, we utilize the intra-view inherent structure information to dynamically identify semantically similar samples within each view. By aggregating their embedding representations, fine-grained structures are enhanced to boost intra-cluster compactness and inter-cluster separation. Then, we construct a cross-view correlation learning module to align semantically related views while preserving the distinctive features of irrelevant views. Based on them, a centralized clustering alignment strategy is proposed to align the similarity distribution and clustering structure between each view and the unified view, balancing the diverse information among multiple views. By jointly training these modules, the unified representation is optimized to capture more discriminative information from multiple views. Extensive experiments conducted on eleven multi-view datasets demonstrate that MISCC outperforms the state-of-the-art clustering methods.

Recently, object detection methods based on multi-modal fusion have gained widespread adoption in autonomous driving, proving to be valuable for detecting objects in dynamic environments. Among them, millimeter wave (mmWave) radar is commonly utilized as an effective complement to cameras, as it is almost unaffected by harsh weather conditions. However, current approaches that fuse mmWave radar and camera often overlook the correlation between the two modalities, failing to fully exploit their complementary features. To address this, we propose a temporal-enhanced radar and camera fusion network to explore the correlation between these two modalities and learn a comprehensive representation for object detection. In our model, a temporal fusion model is introduced to fuse mmWave radar features from different moments, thus mitigating the problem of mmWave radar point-object mismatch due to object movement. Moreover, a new correlation-based fusion strategy using the dedicated mask cross-attention is proposed to fuse mmWave radar and vision features more effectively. Finally, we design a gate feature pyramid network that selects shallow texture information based on deep semantic information to obtain more representative features. The experimental results on the nuScenes benchmark demonstrate the effectiveness of our proposed method.