2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-6.9
Paper Title LIGHTER AND FASTER CROSS-CONCATENATED MULTI-SCALE RESIDUAL BLOCK BASED NETWORK FOR VISUAL SALIENCY PREDICTION
Authors Sai Phani Kumar Malladi, Jayanta Mukhopadhyay, Indian Institute of Technology Kharagpur, India; Mohamed-Chaker Larabi, University of Poitiers, France; Santanu Chaudhury, Indian Institute of Technology Jodhpur, India
SessionARS-6: Image and Video Interpretation and Understanding 1
LocationArea H
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Existing deep architectures for visual saliency prediction face problems like inefficient feature encoding, larger inference times, and a huge number of model parameters. One possible solution is to make the local and global contextual feature extraction computationally less intensive by a novel lighter architecture. In this work, we propose an end-to-end learnable, inter-scale information sharing residual block-based architecture for saliency prediction. A series of these blocks are used for efficient multi-scale feature extraction followed by a dilated inception module (DIM) and a novel decoder. We name this network as cross-concatenated multi-scale residual (CMR) block-based network, CMRNet. We comprehensively evaluate our architecture on three datasets: SALICON, MIT1003, and MIT300. Experimental results show that our model works at par with other state-of-the-art models. Especially, our model outperforms all the other models with a smaller inference time and a lesser number of model parameters.