Motion Guided Token Compression for Efficient Masked Video Modeling

• URL: http://arxiv.org/abs/2402.18577v1
• Date: Wed, 10 Jan 2024 07:49:23 GMT
• Title: Motion Guided Token Compression for Efficient Masked Video Modeling
• Authors: Yukun Feng, Yangming Shi, Fengze Liu, Tan Yan,
• Abstract summary: This paper showcases the enhanced performance achieved through an escalation in the frames per second (FPS) rate. We also present a novel approach, Motion Guided Token Compression (MGTC), to empower Transformer models to utilize a smaller yet more representative set of tokens for comprehensive video representation. Our experiments, conducted on widely examined video recognition datasets, Kinetics-400, UCF101 and HMDB51, demonstrate that elevating the FPS rate results in a significant top-1 accuracy score improvement of over 1.6, 1.6 and 4.0.
• Score: 7.548789718676737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent developments in Transformers have achieved notable strides in enhancing video comprehension. Nonetheless, the O($N^2$) computation complexity associated with attention mechanisms presents substantial computational hurdles when dealing with the high dimensionality of videos. This challenge becomes particularly pronounced when striving to increase the frames per second (FPS) to enhance the motion capturing capabilities. Such a pursuit is likely to introduce redundancy and exacerbate the existing computational limitations. In this paper, we initiate by showcasing the enhanced performance achieved through an escalation in the FPS rate. Additionally, we present a novel approach, Motion Guided Token Compression (MGTC), to empower Transformer models to utilize a smaller yet more representative set of tokens for comprehensive video representation. Consequently, this yields substantial reductions in computational burden and remains seamlessly adaptable to increased FPS rates. Specifically, we draw inspiration from video compression algorithms and scrutinize the variance between patches in consecutive video frames across the temporal dimension. The tokens exhibiting a disparity below a predetermined threshold are then masked. Notably, this masking strategy effectively addresses video redundancy while conserving essential information. Our experiments, conducted on widely examined video recognition datasets, Kinetics-400, UCF101 and HMDB51, demonstrate that elevating the FPS rate results in a significant top-1 accuracy score improvement of over 1.6, 1.6 and 4.0. By implementing MGTC with the masking ratio of 25\%, we further augment accuracy by 0.1 and simultaneously reduce computational costs by over 31\% on Kinetics-400. Even within a fixed computational budget, higher FPS rates paired with MGTC sustain performance gains when compared to lower FPS settings.

Related papers

• Adaptive Caching for Faster Video Generation with Diffusion Transformers [52.73348147077075]
  Diffusion Transformers (DiTs) rely on larger models and heavier attention mechanisms, resulting in slower inference speeds. We introduce a training-free method to accelerate video DiTs, termed Adaptive Caching (AdaCache) We also introduce a Motion Regularization (MoReg) scheme to utilize video information within AdaCache, controlling the compute allocation based on motion content.
  arXiv  Detail & Related papers  (2024-11-04T18:59:44Z)
• SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity [15.872209884833977]
  We propose a memory-efficient scheduling method to eliminate memory overhead and an online adjustment mechanism to minimize accuracy degradation. SparseTem achieves speedup of 1.79x for EfficientDet and 4.72x for CRNN, with minimal accuracy drop and no additional memory overhead.
  arXiv  Detail & Related papers  (2024-10-28T07:13:25Z)
• MaskVD: Region Masking for Efficient Video Object Detection [11.759503235646696]
  Video tasks are compute-heavy and pose a challenge when deploying in real-time applications. This paper presents a strategy for masking regions in video frames. By leveraging extracted features from previous frames, ViT backbones directly benefit from region masking.
  arXiv  Detail & Related papers  (2024-07-16T08:01:49Z)
• Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior [13.595032265551184]
  Video-to-video synthesis poses significant challenges in maintaining character consistency, smooth temporal transitions, and preserving visual quality during fast motion. We propose an adaptive motion-guided cross-frame attention mechanism that selectively reduces redundant computations. This enables a greater number of cross-frame attentions over more frames within the same computational budget.
  arXiv  Detail & Related papers  (2024-06-07T12:12:25Z)
• HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition [11.362605513514943]
  Action recognition in videos poses a challenge due to its high computational cost. We propose HaltingVT, an efficient video transformer adaptively removing redundant video patch tokens. On the Mini-Kinetics dataset, we achieved 75.0% top-1 ACC with 24.2 GFLOPs, as well as 67.2% top-1 ACC with an extremely low 9.9 GFLOPs.
  arXiv  Detail & Related papers  (2024-01-10T07:42:55Z)
• Dynamic Frame Interpolation in Wavelet Domain [57.25341639095404]
  Video frame is an important low-level computation vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
  arXiv  Detail & Related papers  (2023-09-07T06:41:15Z)
• SVFormer: Semi-supervised Video Transformer for Action Recognition [88.52042032347173]
  We introduce SVFormer, which adopts a steady pseudo-labeling framework to cope with unlabeled video samples. In addition, we propose a temporal warping to cover the complex temporal variation in videos. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400.
  arXiv  Detail & Related papers  (2022-11-23T18:58:42Z)
• EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens [57.354304637367555]
  We present EVEREST, a surprisingly efficient MVA approach for video representation learning. It finds tokens containing rich motion features and discards uninformative ones during both pre-training and fine-tuning. Our method significantly reduces the computation and memory requirements of MVA.
  arXiv  Detail & Related papers  (2022-11-19T09:57:01Z)
• It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training [76.69480467101143]
  Self-supervised video transformer pre-training has recently benefited from the mask-and-predict pipeline. We explicitly investigate motion cues in videos as extra prediction target and propose our Masked Appearance-Motion Modeling framework. Our method learns generalized video representations and achieves 82.3% on Kinects-400, 71.3% on Something-Something V2, 91.5% on UCF101, and 62.5% on HMDB51.
  arXiv  Detail & Related papers  (2022-10-11T08:05:18Z)
• Space-time Mixing Attention for Video Transformer [55.50839896863275]
  We propose a Video Transformer model the complexity of which scales linearly with the number of frames in the video sequence. We demonstrate that our model produces very high recognition accuracy on the most popular video recognition datasets.
  arXiv  Detail & Related papers  (2021-06-10T17:59:14Z)

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.