  For Domain Generalizable Object Detection (DGOD), Disentangled Representation
Learning (DRL) helps a lot by explicitly disentangling Domain-Invariant
Representations (DIR) from Domain-Specific Representations (DSR). Considering
the domain category is an attribute of input data, it should be feasible for
networks to fit a specific mapping which projects DSR into feature channels
exclusive to domain-specific information, and thus much cleaner disentanglement
of DIR from DSR can be achieved simply on channel dimension. Inspired by this
idea, we propose a novel DRL method for DGOD, which is termed Gated
Domain-Invariant Feature Disentanglement (GDIFD). In GDIFD, a Channel Gate
Module (CGM) learns to output channel gate signals close to either 0 or 1,
which can mask out the channels exclusive to domain-specific information
helpful for domain recognition. With the proposed GDIFD, the backbone in our
framework can fit the desired mapping easily, which enables the channel-wise
disentanglement. In experiments, we demonstrate that our approach is highly
effective and achieves state-of-the-art DGOD performance.
