  Data in real-world object detection often exhibits the long-tailed
distribution. Existing solutions tackle this problem by mitigating the
competition between the head and tail categories. However, due to the scarcity
of training samples, tail categories are still unable to learn discriminative
representations. Bringing more data into the training may alleviate the
problem, but collecting instance-level annotations is an excruciating task. In
contrast, image-level annotations are easily accessible but not fully
exploited. In this paper, we propose a novel framework CLIS (multi-task
Collaborative Learning with Image-level Supervision), which leverage
image-level supervision to enhance the detection ability in a multi-task
collaborative way. Specifically, there are an object detection task (consisting
of an instance-classification task and a localization task) and an
image-classification task in our framework, responsible for utilizing the two
types of supervision. Different tasks are trained collaboratively by three key
designs: (1) task-specialized sub-networks that learn specific representations
of different tasks without feature entanglement. (2) a siamese sub-network for
the image-classification task that shares its knowledge with the
instance-classification task, resulting in feature enrichment of detectors. (3)
a contrastive learning regularization that maintains representation
consistency, bridging feature gaps of different supervision. Extensive
experiments are conducted on the challenging LVIS dataset. Without
sophisticated loss engineering, CLIS achieves an overall AP of 31.1 with 10.1
point improvement on tail categories, establishing a new state-of-the-art. Code
will be at https://github.com/waveboo/CLIS.
