Vision-language object detectors (VLODs) pretrained on large image-text corpora exhibit strong zero-shot detection abilities, yet their performance often degrades on specialized downstream tasks that arrive sequentially. Hence, we study Incremental Vision-Language Object Detection (IVLOD), which requires continually ad...
Xiang Song, Zi-Ye Yang, Yu-Hang He et al.· IEEE Transactions on Image P...· 0 citations
Experiments show that CoRe substantially outperforms existing VLMs on CoRe-Bench while remaining competitive on standard multimodal benchmarks, achieving a 28.2-point gain in partial accuracy over the strongest baseline.
Lin Peng, Cong Wan, Zeyu Guo et al.· arXiv.org· 0 citations
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domain incremental learning (DIL) addresses this challenge by enabling models to continuously adapt while retaining prior knowledge. Among existi...
Qiang Wang, Songlin Dong, Shao-Kun Wang et al.· 0 citations
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