Search Results for author: Aimin Hao

Found 9 papers, 6 papers with code

Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception

1 code implementation27 Dec 2021 Guotao Wang, Chenglizhao Chen, Deng-Ping Fan, Aimin Hao, Hong Qin

Moreover, we distill knowledge from these regions to obtain complete new spatial-temporal-audio (STA) fixation prediction (FP) networks, enabling broad applications in cases where video tags are not available.

Video Saliency Detection

Knowledge-inspired 3D Scene Graph Prediction in Point Cloud

no code implementations NeurIPS 2021 Shoulong Zhang, Shuai Li, Aimin Hao, Hong Qin

Unlike conventional methods that learn knowledge embedding and regular patterns from encoded visual information, we propose to suppress the misunderstandings caused by appearance similarities and other perceptual confusion.

From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach

1 code implementation CVPR 2021 Guotao Wang, Chenglizhao Chen, Deng-Ping Fan, Aimin Hao, Hong Qin

Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly.

Video Saliency Detection

Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter

no code implementations10 Aug 2020 Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin

In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.

object-detection Re-Ranking +2

A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset

no code implementations7 Aug 2020 Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin

Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.

object-detection RGB Salient Object Detection +1

Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection

1 code implementation7 Aug 2020 Xuehao Wang, Shuai Li, Chenglizhao Chen, Yuming Fang, Aimin Hao, Hong Qin

Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.

object-detection RGB-D Salient Object Detection +1

Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks

1 code implementation7 Aug 2020 Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin

Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.

object-detection RGB Salient Object Detection +1

Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection

1 code implementation7 Aug 2020 Xuehao Wang, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin

Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.

object-detection RGB-D Salient Object Detection +2

A Plug-and-play Scheme to Adapt Image Saliency Deep Model for Video Data

1 code implementation2 Aug 2020 Yunxiao Li, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin

With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.

Video Saliency Detection

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