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- Wangmeng ZuoSuggested from co-authorship
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Works12 from public data
- Blind Super-Resolution With Iterative Kernel Correction590
An iterative correction scheme -- IKC that achieves better results than direct kernel estimation in blind SR problem and an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD.
- Component Divide-and-Conquer for Real-World Image Super-Resolution419
This paper proposes a Component Divide-and-Conquer model and a Gradient-Weighted loss for SR, exploring the utility of guiding SR model with low-level image components, and proposes a feasible way to balance the difficulties of image components for SR.
- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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- Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention11
CogDriving is presented, a novel network designed for synthesizing high-quality multi-view driving videos that leverages a Diffusion Transformer architecture with holistic-4D attention modules, enabling simultaneous associations across the spatial, temporal, and viewpoint dimensions.
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- Modulated Memory Network for Video Object Segmentation2
The Modulated Memory Network (MMN) for VOS enhances matching-based VOS methods in the following ways: introducing an Importance Modulator, which adjusts memory frames using adaptive weight maps generated based on the segmentation confidence associated with each frame, and incorporating a Position Modulator that encodes spatial and temporal positional information for both memory frames and the current frame.
- Two-Stream Networks for Object Segmentation in Videos1
A Two-Stream Network that combines a pixel stream with a conventional pixel-level memory, to segment the seen pixels based on their pixellevel memory retrieval, and an instance stream for the unseen pixels, where a holistic understanding of the instance is obtained with dynamic segmentation heads conditioned on the features of the target instance.
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