  In contrast to the incremental classification task, the incremental detection
task is characterized by the presence of data ambiguity, as an image may have
differently labeled bounding boxes across multiple continuous learning stages.
This phenomenon often impairs the model's ability to effectively learn new
classes. However, existing research has paid less attention to the forward
compatibility of the model, which limits its suitability for incremental
learning. To overcome this obstacle, we propose leveraging a visual-language
model such as CLIP to generate text feature embeddings for different class
sets, which enhances the feature space globally. We then employ super-classes
to replace the unavailable novel classes in the early learning stage to
simulate the incremental scenario. Finally, we utilize the CLIP image encoder
to accurately identify potential objects. We incorporate the finely recognized
detection boxes as pseudo-annotations into the training process, thereby
further improving the detection performance. We evaluate our approach on
various incremental learning settings using the PASCAL VOC 2007 dataset, and
our approach outperforms state-of-the-art methods, particularly for recognizing
the new classes.
