  Localizing and recognizing objects in the open-ended physical world poses a
long-standing challenge within the domain of machine perception. Recent methods
have endeavored to address the issue by employing a class-agnostic mask (or
box) proposal model, complemented by an open-vocabulary classifier (e.g., CLIP)
using pre-extracted text embeddings. However, it is worth noting that these
open-vocabulary recognition models still exhibit limitations in practical
applications. On one hand, they rely on the provision of class names during
testing, where the recognition performance heavily depends on this predefined
set of semantic classes by users. On the other hand, when training with
multiple datasets, human intervention is required to alleviate the label
definition conflict between them. In this paper, we introduce the OmniScient
Model (OSM), a novel Large Language Model (LLM) based mask classifier, as a
straightforward and effective solution to the aforementioned challenges.
Specifically, OSM predicts class labels in a generative manner, thus removing
the supply of class names during both training and testing. It also enables
cross-dataset training without any human interference, exhibiting robust
generalization capabilities due to the world knowledge acquired from the LLM.
By combining OSM with an off-the-shelf mask proposal model, we present
promising results on various benchmarks, and demonstrate its effectiveness in
handling novel concepts. Code/model are available at
https://github.com/bytedance/OmniScient-Model.
