Unsupervised Video Decomposition using Spatio-temporal Iterative Inference

• URL: http://arxiv.org/abs/2006.14727v1
• Date: Thu, 25 Jun 2020 22:57:17 GMT
• Title: Unsupervised Video Decomposition using Spatio-temporal Iterative Inference
• Authors: Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann
• Abstract summary: Multi-object scene decomposition is a fast-emerging problem in learning. We show that our model has a high accuracy even without color information. We demonstrate the decomposition, segmentation prediction capabilities of our model and show that it outperforms the state-of-the-art on several benchmark datasets.
• Score: 31.97227651679233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised multi-object scene decomposition is a fast-emerging problem in representation learning. Despite significant progress in static scenes, such models are unable to leverage important dynamic cues present in video. We propose a novel spatio-temporal iterative inference framework that is powerful enough to jointly model complex multi-object representations and explicit temporal dependencies between latent variables across frames. This is achieved by leveraging 2D-LSTM, temporally conditioned inference and generation within the iterative amortized inference for posterior refinement. Our method improves the overall quality of decompositions, encodes information about the objects' dynamics, and can be used to predict trajectories of each object separately. Additionally, we show that our model has a high accuracy even without color information. We demonstrate the decomposition, segmentation, and prediction capabilities of our model and show that it outperforms the state-of-the-art on several benchmark datasets, one of which was curated for this work and will be made publicly available.

Related papers

• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling [67.94143911629143]
  We propose a generative Transformer VAE architecture to model hand pose and action. To faithfully model the semantic dependency and different temporal granularity of hand pose and action, we decompose the framework into two cascaded VAE blocks. Results show that our joint modeling of recognition and prediction improves over isolated solutions.
  arXiv  Detail & Related papers  (2023-11-29T05:28:39Z)
• Enhanced Spatio-Temporal Context for Temporally Consistent Robust 3D Human Motion Recovery from Monocular Videos [5.258814754543826]
  We propose a novel method for temporally consistent motion estimation from a monocular video. Instead of using generic ResNet-like features, our method uses a body-aware feature representation and an independent per-frame pose. Our method attains significantly lower acceleration error and outperforms the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-20T10:53:59Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations [11.486068333583216]
  This paper tackles the problem of learning effective models to reconstruct missing data points. We propose a class of attention-based architectures, that given a set of highly sparse observations, learn a representation for points in time and space. Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies.
  arXiv  Detail & Related papers  (2022-05-26T16:40:48Z)
• Temporal Relevance Analysis for Video Action Models [70.39411261685963]
  We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models. We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected.
  arXiv  Detail & Related papers  (2022-04-25T19:06:48Z)
• Exploring Motion and Appearance Information for Temporal Sentence Grounding [52.01687915910648]
  We propose a Motion-Appearance Reasoning Network (MARN) to solve temporal sentence grounding. We develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations. Our proposed MARN significantly outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-01-03T02:44:18Z)
• Recur, Attend or Convolve? Frame Dependency Modeling Matters for Cross-Domain Robustness in Action Recognition [0.5448283690603357]
  Previous results have shown that 2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather than shape for various computer vision tasks. This raises suspicion that large video models learn spurious correlations rather than to track relevant shapes over time. We study the cross-domain robustness for recurrent, attention-based and convolutional video models, respectively, to investigate whether this robustness is influenced by the frame dependency modeling.
  arXiv  Detail & Related papers  (2021-12-22T19:11:53Z)
• Coarse-Fine Networks for Temporal Activity Detection in Videos [45.03545172714305]
  We introduce 'Co-Fine Networks', a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion. We show that our method can outperform the state-of-the-arts for action detection in public datasets with a significantly reduced compute and memory footprint.
  arXiv  Detail & Related papers  (2021-03-01T20:48:01Z)
• Learning Temporal Dynamics from Cycles in Narrated Video [85.89096034281694]
  We propose a self-supervised solution to the problem of learning to model how the world changes as time elapses. Our model learns modality-agnostic functions to predict forward and backward in time, which must undo each other when composed. We apply the learned dynamics model without further training to various tasks, such as predicting future action and temporally ordering sets of images.
  arXiv  Detail & Related papers  (2021-01-07T02:41:32Z)
• Interpretable Deep Representation Learning from Temporal Multi-view Data [4.2179426073904995]
  We propose a generative model based on variational autoencoder and a recurrent neural network to infer the latent dynamics for multi-view temporal data. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.
  arXiv  Detail & Related papers  (2020-05-11T15:59:06Z)

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.