Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-driven Approach

• URL: http://arxiv.org/abs/2303.10372v1
• Date: Sat, 18 Mar 2023 09:36:59 GMT
• Title: Just Noticeable Visual Redundancy Forecasting: A Deep Multimodal-driven Approach
• Authors: Wuyuan Xie, Shukang Wang, Sukun Tian, Lirong Huang, Ye Liu, Miaohui Wang
• Abstract summary: Just noticeable difference (JND) refers to the maximum visual change that human eyes cannot perceive. In this article, we investigate the JND modeling from an end-to-end multimodal perspective, namely hmJND-Net.
• Score: 11.600496805298778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Just noticeable difference (JND) refers to the maximum visual change that human eyes cannot perceive, and it has a wide range of applications in multimedia systems. However, most existing JND approaches only focus on a single modality, and rarely consider the complementary effects of multimodal information. In this article, we investigate the JND modeling from an end-to-end homologous multimodal perspective, namely hmJND-Net. Specifically, we explore three important visually sensitive modalities, including saliency, depth, and segmentation. To better utilize homologous multimodal information, we establish an effective fusion method via summation enhancement and subtractive offset, and align homologous multimodal features based on a self-attention driven encoder-decoder paradigm. Extensive experimental results on eight different benchmark datasets validate the superiority of our hmJND-Net over eight representative methods.

Related papers

• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness [2.341385717236931]
  We propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. Our HiDAnet performs favorably over the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2023-01-18T10:00:59Z)
• Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking [23.130490413184596]
  We introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion. Our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion.
  arXiv  Detail & Related papers  (2022-03-30T13:00:27Z)
• Multistage linguistic conditioning of convolutional layers for speech emotion recognition [7.482371204083917]
  We investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER) We propose a novel, multistage fusion method where the two information streams are integrated in several layers of a deep neural network (DNN) Experiments on the widely used IEMOCAP and MSP-Podcast databases demonstrate that the two fusion methods clearly outperform a shallow (late) fusion baseline.
  arXiv  Detail & Related papers  (2021-10-13T11:28:04Z)
• Multimodal Object Detection via Bayesian Fusion [59.31437166291557]
  We study multimodal object detection with RGB and thermal cameras, since the latter can provide much stronger object signatures under poor illumination. Our key contribution is a non-learned late-fusion method that fuses together bounding box detections from different modalities. We apply our approach to benchmarks containing both aligned (KAIST) and unaligned (FLIR) multimodal sensor data.
  arXiv  Detail & Related papers  (2021-04-07T04:03:20Z)
• Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition [89.0152015268929]
  We propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the 3D Central Difference Convolution (3D-CDC) family, and optimized backbones for multi-modal-rate branches and lateral connections. The resultant multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics.
  arXiv  Detail & Related papers  (2020-08-21T10:45:09Z)
• A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation [131.33610549540043]
  We propose a novel graph-based multi-modal fusion encoder for NMT. We first represent the input sentence and image using a unified multi-modal graph. We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations.
  arXiv  Detail & Related papers  (2020-07-17T04:06:09Z)

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.