MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation

• URL: http://arxiv.org/abs/2506.23151v1
• Date: Sun, 29 Jun 2025 09:01:42 GMT
• Title: MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation
• Authors: Vladislav Bargatin, Egor Chistov, Alexander Yakovenko, Dmitriy Vatolin,
• Abstract summary: MEMFOF is a memory-efficient multi-frame optical flow method.<n>Method requires only 2.09 GB of GPU memory at runtime for 1080p inputs, and 28.5 GB during training.<n>Method ranks first on the Spring benchmark with a 1-pixel (1px) outlier rate of 3.289.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in optical flow estimation have prioritized accuracy at the cost of growing GPU memory consumption, particularly for high-resolution (FullHD) inputs. We introduce MEMFOF, a memory-efficient multi-frame optical flow method that identifies a favorable trade-off between multi-frame estimation and GPU memory usage. Notably, MEMFOF requires only 2.09 GB of GPU memory at runtime for 1080p inputs, and 28.5 GB during training, which uniquely positions our method to be trained at native 1080p without the need for cropping or downsampling. We systematically revisit design choices from RAFT-like architectures, integrating reduced correlation volumes and high-resolution training protocols alongside multi-frame estimation, to achieve state-of-the-art performance across multiple benchmarks while substantially reducing memory overhead. Our method outperforms more resource-intensive alternatives in both accuracy and runtime efficiency, validating its robustness for flow estimation at high resolutions. At the time of submission, our method ranks first on the Spring benchmark with a 1-pixel (1px) outlier rate of 3.289, leads Sintel (clean) with an endpoint error (EPE) of 0.963, and achieves the best Fl-all error on KITTI-2015 at 2.94%. The code is available at https://github.com/msu-video-group/memfof.

Related papers

• Efficient Correlation Volume Sampling for Ultra-High-Resolution Optical Flow Estimation [10.244450933742089]
  We propose a more efficient implementation of the all-pairs correlation volume sampling, still matching the exact mathematical operator as defined by RAFT.<n>Our approach outperforms on-demand sampling by up to 90% while maintaining low memory usage, and performs on par with the default implementation with up to 95% lower memory usage.
  arXiv  Detail & Related papers  (2025-05-22T17:30:38Z)
• Dataset Distillation with Neural Characteristic Function: A Minmax Perspective [39.77640775591437]
  We reformulate dataset distillation as a minmax optimization problem and introduce Neural Characteristic Function Discrepancy (NCFD)<n>NCFD is a comprehensive and theoretically grounded metric for measuring distributional differences.<n>Our method achieves significant performance gains over state-of-the-art methods on both low- and high-resolution datasets.
  arXiv  Detail & Related papers  (2025-02-28T02:14:55Z)
• Progressive Mixed-Precision Decoding for Efficient LLM Inference [49.05448842542558]
  We introduce Progressive Mixed-Precision Decoding (PMPD) to address the memory-boundedness of decoding.<n>PMPD achieves 1.4$-$12.2$times$ speedup in matrix-vector multiplications over fp16 models.<n>Our approach delivers a throughput gain of 3.8$-$8.0$times$ over fp16 models and up to 1.54$times$ over uniform quantization approaches.
  arXiv  Detail & Related papers  (2024-10-17T11:46:33Z)
• LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs [4.536118764799076]
  Fine-tuning pre-trained large language models with limited hardware presents challenges due to GPU memory constraints. We introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods. We show that LLMem accurately estimates peak GPU memory usage on a single GPU, with error rates of up to 1.6%.
  arXiv  Detail & Related papers  (2024-04-16T22:11:35Z)
• MemFlow: Optical Flow Estimation and Prediction with Memory [54.22820729477756]
  We present MemFlow, a real-time method for optical flow estimation and prediction with memory. Our method enables memory read-out and update modules for aggregating historical motion information in real-time. Our approach seamlessly extends to the future prediction of optical flow based on past observations.
  arXiv  Detail & Related papers  (2024-04-07T04:56:58Z)
• SqueezeLLM: Dense-and-Sparse Quantization [80.32162537942138]
  Main bottleneck for generative inference with LLMs is memory bandwidth, rather than compute, for single batch inference. We introduce SqueezeLLM, a post-training quantization framework that enables lossless compression to ultra-low precisions of up to 3-bit. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which searches for the optimal bit precision assignment based on second-order information; and (ii) the Dense-and-Sparse decomposition that stores outliers and sensitive weight values in an efficient sparse format.
  arXiv  Detail & Related papers  (2023-06-13T08:57:54Z)
• DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow [7.73554718719193]
  We propose a novel Patchmatch-based framework to work on high-resolution optical flow estimation. It can get high-precision results with lower memory benefiting from propagation and local search of Patchmatch. Our method ranks first on all the metrics on the popular KITTI2015 benchmark, and ranks second on EPE on the Sintel clean benchmark among published optical flow methods.
  arXiv  Detail & Related papers  (2022-04-01T10:13:59Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• ScopeFlow: Dynamic Scene Scoping for Optical Flow [94.42139459221784]
  We propose to modify the common training protocols of optical flow. The improvement is based on observing the bias in sampling challenging data. We find that both regularization and augmentation should decrease during the training protocol.
  arXiv  Detail & Related papers  (2020-02-25T09:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.