12 accepted papers
Sketch-based person re-identification aims to match hand-drawn sketches with RGB surveillance images, but remains challenging due to severe modality gaps and limited labeled data. To address this, we propose KTCAA, a theoretically inspired framework for few-shot cross-modal generalization. Drawing o
Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Yet, data itself remains an underexplored factor in this process. We revisit this task from a data-centric perspective: Can effective data s
Federated Incremental Learning (FIL) aims to learn streaming tasks across distributed clients without catastrophic forgetting while preserving privacy. Most existing methods focus on sample-based replay techniques, which mitigate forgetting by replaying historical data samples. However, such methods…
LiDAR-event camera integration has shown considerable promise and is gaining traction across various perception applications. Event cameras offer high temporal resolution and wide dynamic range but suffer from noise sensitivity and lack depth information. LiDAR complements these capabilities by prov…
3D semantic segmentation serves as a fundamental component in many applications, such as autonomous driving and medical image analysis. Although recent methods have advanced the field, adapting these methods to new environments or object categories without extensive retraining remains a significant
The progress in materials science and drug discovery is impeded by the availability of labeled data and the high costs of manual annotation, driving the need for efficient strategies to capture molecular representations and enable accurate predictions. Pretrained Graph Neural Networks have shown pro…
Composed Image Retrieval (CIR) aims to retrieve a target image using a query that combines a reference image and a textual description, benefiting users to express their intent more effectively. Despite significant advances in CIR methods, two unresolved problems remain: 1) existing methods overlook…
Federated Continual Learning (FCL) has recently garnered significant attention due to its ability to continuously learn new tasks while protecting user privacy. However, existing Data-Free Knowledge Transfer (DFKT) methods require training the entire model, leading to high training and communication…
Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance furth…
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified persons in infrared images to visible images without annotations, and vice versa. USVI-ReID is a challenging yet underexplored task. Most existing methods address the USVI-ReID through cluster-based contrastiv…
"Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations…
Visible-infrared person re-identification (VI-ReID) aims to match a specific person from a gallery of images captured from non-overlapping visible and infrared cameras. Most works focus on fully supervised VI-ReID, which requires substantial cross-modality annotation that is more expensive than the…