Enhancing Object Detection with Active Exploration and Spatiotemporal Aggregation

Classic object detectors are fundamentally limited by their reliance on a single, static viewpoint, which often suffers from occlusions, challenging scales, and ambiguous perspectives. Video-based object detectors can partially mitigate this problem by aggregating information from multiple views, but they usually work with passively gathered sequences and are in no means guaranteed to provide sufficient information for all objects of interest. On the other hand, active vision methods can seek out better views, yet existing active object detectors typically only try to search for a single optimal perspective and discard valuable information gathered along their path. In this paper, we introduce a new paradigm for object detection that unifies active exploration with cumulative spatiotemporal aggregation. We train an embodied agent to intelligently explore its environment, guided by a novel, detection-aware reward function that directly encourages seeking out views that resolve visual ambiguities. To leverage this active exploration, we introduce a robust aggregation pipeline that adeptly harnesses SAM 2's temporal reasoning capabilities to fuse information from the agent's entire trajectory into a single, coherent set of detections. Through extensive experiments on AI2-THOR, we demonstrate that our framework provides a consistent and substantial performance uplift when applied to a wide range of state-of-the-art detectors, establishing a strong and versatile new baseline for the next generation of active vision systems.