IROS Conference 2025 Conference Paper
ETA: Learning Optical Flow with Efficient Temporal Attention
- Bo Wang 0144
- Zhenping Sun
- Yang Yu 0014
- Li Liu 0002
- Jian Li 0003
- Dewen Hu
Considering the potential of using multi-frame information to solve the occlusion problem, we introduce a novel idea of multi-frame information integration, which uses the attention mechanism to fuse the temporal information from the previous frame. The idea can effectively improve the estimation accuracy in occluded regions and optimize the inference speed under multi-frame settings. Meanwhile, we suggest the concept of attention confidence to provide an explicit value criterion for the model to utilize useful attention information more efficiently. Furthermore, we propose an Efficient Temporal Attention network (ETA), which achieves promising results on Sintel and KITTI benchmarks, especially with a 9. 4% error reduction compared to the baseline method GMA on Sintel (test) Clean.