Vehicle detection is an important component of intelligent transportation systems and
autonomous driving. However, in real-world vehicle detection scenarios, the presence of …

Abstract Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has
recently become a popular research subject in artificial intelligence and cognitive …

Restoring old photographs can preserve cherished memories. Previous methods handled
diverse damages within the same network structure, which proved impractical. In addition …

Proper interaction between visual and semantic features is crucial to obtain a powerful
feature representation for scene text recognition (STR). The existing interaction methods …

With the significant advancements of Large Language Models (LLMs) in the field of Natural
Language Processing (NLP), the development of image-text multimodal models has …

We propose SparseDC, a model for Depth Completion from Sparse and non-uniform inputs.
Unlike previous methods focusing on completing fixed distributions on benchmark datasets …

In the era of big data, data are being collected, stored, and analyzed at an unprecedented
rate. Owing to the limitations of the quantity, diversity, and complexity of data, traditional data …

Unsupervised domain adaptation (UDA) aims to transfer knowledge acquired from the
labeled source domain to the unlabeled target domain. However, the quality of samples can …

Open-vocabulary learning can identify categories marked during training (seen categories)
and generalize to categories not annotated in the training set (unseen categories). It could …

Deep learning-based methods have recently shown great promise in medical image
segmentation task. However, CNN-based frameworks struggle with inadequate long-range …