.png){kind=icon}
返回
Transformer-based Cross Reference Network for video salient object detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2022.06.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Video salient object detection is a fundamental computer vision task aimed at highlighting the most conspicuous objects in a video sequence. There are two key challenges presented in video salient ob-ject detection: (1) how to extract effective feature representations from appearance and motion cues, and (2) how to combine both of them into robust saliency representation. To handle these challenges, in this paper, we propose a novel Transformer-based Cross Reference Network (TCRN), which fully exploits long-range context dependencies in both feature representation extraction and cross-modal (i.e., appear-ance and motion) integration. In contrast to existing CNN-based methods, our approach formulates video salient object detection as a sequence-to-sequence prediction task. In the proposed approach, the deep feature extraction is achieved by a pure vision transformer with multi-resolution token representations. Specifically, we design a Gated Cross Reference (GCR) module to effectively integrate appearance and motion into saliency representation. The GCR first propagates global context information between differ-ent modalities, and then perform cross-modal fusion by a gate mechanism. Extensive evaluations on five widely-used benchmarks show that the proposed Transformer-based method performs favorably against the existing state-of-the-art methods (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Video salient
Object detection
Transformer
Cross -modal integration
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Dispersion relations and causality: does relativistic causality require thatn(ω) → 1 as ω → ∞ ?
PREAI
分享收藏Radiation‐induced sensitivity of tissue‐resident mesenchymal stem cells in the head and neck region
PREAI
分享收藏
查看更多内容