Collaborative perception is dedicated to tackling the constraints of single-agent perception, such as occlusions, based on the multiple agents' multi-view sensor inputs. However, most existing works assume an ideal condition that all agents'

We further extend Kaleidoscope to critic ensembles in the context of actor-critic algorithms, which could help improve value estimations.

However, inheriting such benefits of CLIP in the fashion domain is non-trivial in the presence of the notable domain gap.

However, the model size and corresponding computational complexity are constantly scaled up in pursuit of higher performance.

Finding specific preference-guided Pareto solutions that represent different tradeoffs among multiple objectives is critical yet challenging in multi-objective problems. Existing methods are restrictive in preference definitions and/or their theoretical guarantees.