  We present LOWA, a novel method for localizing objects with attributes
effectively in the wild. It aims to address the insufficiency of current
open-vocabulary object detectors, which are limited by the lack of
instance-level attribute classification and rare class names. To train LOWA, we
propose a hybrid vision-language training strategy to learn object detection
and recognition with class names as well as attribute information. With LOWA,
users can not only detect objects with class names, but also able to localize
objects by attributes. LOWA is built on top of a two-tower vision-language
architecture and consists of a standard vision transformer as the image encoder
and a similar transformer as the text encoder. To learn the alignment between
visual and text inputs at the instance level, we train LOWA with three training
steps: object-level training, attribute-aware learning, and free-text joint
training of objects and attributes. This hybrid training strategy first ensures
correct object detection, then incorporates instance-level attribute
information, and finally balances the object class and attribute sensitivity.
We evaluate our model performance of attribute classification and attribute
localization on the Open-Vocabulary Attribute Detection (OVAD) benchmark and
the Visual Attributes in the Wild (VAW) dataset, and experiments indicate
strong zero-shot performance. Ablation studies additionally demonstrate the
effectiveness of each training step of our approach.
