Temporal-adaptive sparse feature aggregation for video object detection

Highlights：

• We propose a novel temporal-adaptive sparse feature aggregation framework for challenging video object detection.

• A stride predictor is proposed to adaptively select the aggregated frames with a temporal-adaptive sparse sampling strategy.

• A pixel-adaptive aggregation module is proposed to enhance pixel feature quality with aligned feature of nearby frames.

• An object-relational aggregation module is proposed to further enhance the proposal features with a graph-based module.

• Our framework aggregates fewer frames than traditional dense aggregation methods and achieves state-of-the-art performance.