Trusted Video Inpainting Localization via Deep Attentive Noise Learning

• URL: http://arxiv.org/abs/2406.13576v1
• Date: Wed, 19 Jun 2024 14:08:58 GMT
• Title: Trusted Video Inpainting Localization via Deep Attentive Noise Learning
• Authors: Zijie Lou, Gang Cao, Man Lin,
• Abstract summary: We present a Trusted Video Inpainting localization network (TruVIL) with excellent robustness and generalization ability. We design deep attentive noise learning in multiple stages to capture the inpainted traces. To prepare enough training samples, we also build a frame-level video object segmentation dataset of 2500 videos.
• Score: 2.1210527985139227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital video inpainting techniques have been substantially improved with deep learning in recent years. Although inpainting is originally designed to repair damaged areas, it can also be used as malicious manipulation to remove important objects for creating false scenes and facts. As such it is significant to identify inpainted regions blindly. In this paper, we present a Trusted Video Inpainting Localization network (TruVIL) with excellent robustness and generalization ability. Observing that high-frequency noise can effectively unveil the inpainted regions, we design deep attentive noise learning in multiple stages to capture the inpainting traces. Firstly, a multi-scale noise extraction module based on 3D High Pass (HP3D) layers is used to create the noise modality from input RGB frames. Then the correlation between such two complementary modalities are explored by a cross-modality attentive fusion module to facilitate mutual feature learning. Lastly, spatial details are selectively enhanced by an attentive noise decoding module to boost the localization performance of the network. To prepare enough training samples, we also build a frame-level video object segmentation dataset of 2500 videos with pixel-level annotation for all frames. Extensive experimental results validate the superiority of TruVIL compared with the state-of-the-arts. In particular, both quantitative and qualitative evaluations on various inpainted videos verify the remarkable robustness and generalization ability of our proposed TruVIL. Code and dataset will be available at https://github.com/multimediaFor/TruVIL.

Related papers

• Video Inpainting Localization with Contrastive Learning [2.1210527985139227]
  Deep inpainting is typically used as malicious manipulation to remove important objects for creating fake videos. This letter proposes a simple yet effective scheme for Video Inpainting with ContrAstive Learning (ViLocal)
  arXiv  Detail & Related papers  (2024-06-25T15:15:54Z)
• Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning [19.346284003982035]
  We propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods.
  arXiv  Detail & Related papers  (2023-03-13T17:45:39Z)
• Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid [102.24539566851809]
  Restoring reasonable and realistic content for arbitrary missing regions in images is an important yet challenging task. Recent image inpainting models have made significant progress in generating vivid visual details, but they can still lead to texture blurring or structural distortions. We propose the Semantic Pyramid Network (SPN) motivated by the idea that learning multi-scale semantic priors can greatly benefit the recovery of locally missing content in images.
  arXiv  Detail & Related papers  (2021-12-08T04:33:33Z)
• Attention-guided Temporal Coherent Video Object Matting [78.82835351423383]
  We propose a novel deep learning-based object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal aggregation module that maximizes image matting networks' strength. We show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network.
  arXiv  Detail & Related papers  (2021-05-24T17:34:57Z)
• Deep Video Inpainting Detection [95.36819088529622]
  Video inpainting detection localizes an inpainted region in a video both spatially and temporally. VIDNet, Video Inpainting Detection Network, contains a two-stream encoder-decoder architecture with attention module.
  arXiv  Detail & Related papers  (2021-01-26T20:53:49Z)
• Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition [24.220358793070965]
  We introduce a new module to improve the capability of 3D convolution (emphLS3D-Conv) We add learnable 2D offsets to 3D convolution which aims to sample locations on spatial feature maps across frames. The experiments on video, video super-resolution, video denoising, and action recognition demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-22T09:20:49Z)
• Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics [74.6968179473212]
  This paper proposes a novel pretext task to address the self-supervised learning problem. We compute a series of partitioning-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion. A neural network is built and trained to yield the statistical summaries given the video frames as inputs.
  arXiv  Detail & Related papers  (2020-08-31T08:31:56Z)
• Neural Sparse Voxel Fields [151.20366604586403]
  We introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF(Mildenhall et al., 2020)) at inference time while achieving higher quality results.
  arXiv  Detail & Related papers  (2020-07-22T17:51:31Z)
• Learning Joint Spatial-Temporal Transformations for Video Inpainting [58.939131620135235]
  We propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. We simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss.
  arXiv  Detail & Related papers  (2020-07-20T16:35:48Z)

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.