Efficient Video Object Segmentation via Modulated Cross-Attention Memory

• URL: http://arxiv.org/abs/2403.17937v3
• Date: Thu, 26 Sep 2024 07:23:49 GMT
• Title: Efficient Video Object Segmentation via Modulated Cross-Attention Memory
• Authors: Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan,
• Abstract summary: We propose a transformer-based approach, named MAVOS, to model temporal smoothness without requiring frequent memory expansion. Our MAVOS achieves a J&F score of 63.3% while operating at 37 frames per second (FPS) on a single V100 GPU.
• Score: 123.12273176475863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due to increased GPU memory demands, as they frequently expand the memory bank every few frames. We propose a transformer-based approach, named MAVOS, that introduces an optimized and dynamic long-term modulated cross-attention (MCA) memory to model temporal smoothness without requiring frequent memory expansion. The proposed MCA effectively encodes both local and global features at various levels of granularity while efficiently maintaining consistent speed regardless of the video length. Extensive experiments on multiple benchmarks, LVOS, Long-Time Video, and DAVIS 2017, demonstrate the effectiveness of our proposed contributions leading to real-time inference and markedly reduced memory demands without any degradation in segmentation accuracy on long videos. Compared to the best existing transformer-based approach, our MAVOS increases the speed by 7.6x, while significantly reducing the GPU memory by 87% with comparable segmentation performance on short and long video datasets. Notably on the LVOS dataset, our MAVOS achieves a J&F score of 63.3% while operating at 37 frames per second (FPS) on a single V100 GPU. Our code and models will be publicly available at: https://github.com/Amshaker/MAVOS.

Related papers

• LiVOS: Light Video Object Segmentation with Gated Linear Matching [116.58237547253935]
  LiVOS is a lightweight memory network that employs linear matching via linear attention. For longer and higher-resolution videos, it matched STM-based methods with 53% less GPU memory and supports 4096p inference on a 32G consumer-grade GPU.
  arXiv  Detail & Related papers  (2024-11-05T05:36:17Z)
• VideoLLaMB: Long-context Video Understanding with Recurrent Memory Bridges [42.555895949250704]
  VideoLLaMB is a novel framework that utilizes temporal memory tokens within bridge layers to allow for the encoding of entire video sequences. SceneTilling algorithm segments videos into independent semantic units to preserve semantic integrity. In terms of efficiency, VideoLLaMB, trained on 16 frames, supports up to 320 frames on a single Nvidia A100 GPU.
  arXiv  Detail & Related papers  (2024-09-02T08:52:58Z)
• Streaming Long Video Understanding with Large Language Models [83.11094441893435]
  VideoStreaming is an advanced vision-language large model (VLLM) for video understanding. It capably understands arbitrary-length video with a constant number of video streaming tokens encoded and propagatedly selected. Our model achieves superior performance and higher efficiency on long video benchmarks.
  arXiv  Detail & Related papers  (2024-05-25T02:22:09Z)
• READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation [24.813416082160224]
  We present READMem, a modular framework for sVOS methods to handle unconstrained videos. We propose a robust association of the embeddings stored in the memory with query embeddings during the update process. Our approach achieves competitive results on the Long-time Video dataset (LV1) while not hindering performance on short sequences.
  arXiv  Detail & Related papers  (2023-05-22T08:31:16Z)
• Region Aware Video Object Segmentation with Deep Motion Modeling [56.95836951559529]
  Region Aware Video Object (RAVOS) is a method that predicts regions of interest for efficient object segmentation and memory storage. For efficient segmentation, object features are extracted according to the ROIs, and an object decoder is designed for object-level segmentation. For efficient memory storage, we propose motion path memory to filter out redundant context by memorizing the features within the motion path of objects between two frames.
  arXiv  Detail & Related papers  (2022-07-21T01:44:40Z)
• Learning Quality-aware Dynamic Memory for Video Object Segmentation [32.06309833058726]
  We propose a Quality-aware Dynamic Memory Network (QDMN) to evaluate the segmentation quality of each frame. Our QDMN achieves new state-of-the-art performance on both DAVIS and YouTube-VOS benchmarks.
  arXiv  Detail & Related papers  (2022-07-16T12:18:04Z)
• XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model [137.50614198301733]
  We present XMem, a video object segmentation architecture for long videos with unified feature memory stores. We develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores. XMem greatly exceeds state-of-the-art performance on long-video datasets.
  arXiv  Detail & Related papers  (2022-07-14T17:59:37Z)
• MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition [74.35009770905968]
  We build a memory-augmented vision transformer that has a temporal support 30x longer than existing models. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets.
  arXiv  Detail & Related papers  (2022-01-20T18:59:54Z)

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.