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
this https URL

现代应用越来越需要适应训练过程中未遇到的新概念的灵活计算机视觉模型。本文通过对开放词汇物体识别限制的详细研究，发现了这些限制的根本原因，并试图理解是否存在于 CLIP 嵌入中的细粒度知识未能在推断时利用。我们的初步实验表明，简单的 CLIP 潜空间重新投影有助于分离细粒度概念，为开发能够处理细节的骨干网络铺平了道路。