MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection


  • Guanxiong Sun Queen's University Belfast AnyVision
  • Yang Hua Queen's University Belfast
  • Guosheng Hu AnyVision Queen's University Belfast
  • Neil Robertson Queen's University Belfast



General, Video Understanding & Activity Analysis, Object Detection & Categorization


State-of-the-art video object detection methods maintain a memory structure, either a sliding window or a memory queue, to enhance the current frame using attention mechanisms. However, we argue that these memory structures are not efficient or sufficient because of two implied operations: (1) concatenating all features in memory for enhancement, leading to a heavy computational cost; (2) frame-wise memory updating, preventing the memory from capturing more temporal information. In this paper, we propose a multi-level aggregation architecture via memory bank called MAMBA. Specifically, our memory bank employs two novel operations to eliminate disadvantages of existing methods: (1) light-weight key-set construction which can significantly reduce the computational cost; (2) fine-grained feature-wise updating strategy which enables our method to utilize knowledge from the whole video. To better enhance features from complementary levels, i.e., feature maps and proposals, we further propose a generalized enhancement operation (GEO) to aggregate multi-level features in a unified manner. We conduct extensive evaluations on the challenging ImageNetVID dataset. Compared with existing state-of-the-art methods, our method achieves superior performance in terms of both speed and accuracy. More remarkably, MAMBA achieves mAP of 83.7%/84.6% at 12.6/9.1 FPS with ResNet-101.




How to Cite

Sun, G., Hua, Y., Hu, G., & Robertson, N. (2021). MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2620-2627.



AAAI Technical Track on Computer Vision II