  Modern applications increasingly demand flexible computer vision models that
adapt to novel concepts not encountered during training. This necessity is
pivotal in emerging domains like extended reality, robotics, and autonomous
driving, which require the ability to respond to open-world stimuli. A key
ingredient is the ability to identify objects based on free-form textual
queries defined at inference time - a task known as open-vocabulary object
detection. Multimodal backbones like CLIP are the main enabling technology for
current open-world perception solutions. Despite performing well on generic
queries, recent studies highlighted limitations on the fine-grained recognition
capabilities in open-vocabulary settings - i.e., for distinguishing subtle
object features like color, shape, and material. In this paper, we perform a
detailed examination of these open-vocabulary object recognition limitations to
find the root cause. We evaluate the performance of CLIP, the most commonly
used vision-language backbone, against a fine-grained object-matching
benchmark, revealing interesting analogies between the limitations of
open-vocabulary object detectors and their backbones. Experiments suggest that
the lack of fine-grained understanding is caused by the poor separability of
object characteristics in the CLIP latent space. Therefore, we try to
understand whether fine-grained knowledge is present in CLIP embeddings but not
exploited at inference time due, for example, to the unsuitability of the
cosine similarity matching function, which may discard important object
characteristics. Our preliminary experiments show that simple CLIP latent-space
re-projections help separate fine-grained concepts, paving the way towards the
development of backbones inherently able to process fine-grained details. The
code for reproducing these experiments is available at
https://github.com/lorebianchi98/FG-CLIP.
