Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
This addresses the long-tail issue in scene graph generation for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the long-tail distribution problem in scene graph generation by learning scene-object interactions and transferring knowledge from head to tail relationships, achieving state-of-the-art performance on the Visual Genome benchmark across three tasks.
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors.