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- Max Q.‐H. MengSuggested from co-authorship
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Works171 from public data
- Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer417
This paper proposes a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and themodality-specific characteristics to boost the reidentification performance.
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- Texture analysis for ulcer detection in capsule endoscopy images188
A new idea of curvelet based local binary pattern is proposed as textural features to distinguish ulcer regions from normal regions, which makes full use of Curvelet transformation and local binary patterns.
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- ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design167
A dedicated algorithm and accelerator co-design framework dubbed ViTCoD for accelerating ViTs and develops a dedicated accelerator to simultaneously coordinate the aforementioned enforced denser and sparser workloads for boosted hardware utilization, while integrating on-chip encoder and decoder engines to leverage ViTCs algorithm pipeline for much reduced data movements.
- Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments160
Experimental results on the present image data of bleeding and ulcer show that it is feasible to exploit the proposed chromaticity moments to detect bleeding and Ulcer for WCE images.
- $\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search155
A simple-but-efficient regularization method, termed as Beta-Decay, is proposed to regularize the DARTS-based NAS searching process and can impose constraints to keep the value and variance of activated architecture parameters from too large.
- Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation146
SelfSupervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domainadaptation (DA) approaches.
- Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning145
The results of numerical simulations reveal that the proposed Kinematic Constrained Bi-directional Rapidly-exploring Random Tree with Efficient Branch Pruning (KB-RRT*) can achieve better performance compared with the conventional path planning algorithms.
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- A novel license plate location method based on wavelet transform and EMD analysis110
A robust method based on wavelet transform and empirical mode decomposition (EMD) analysis to search for the location of a license plate in an image to deal with some challenging problems in practice such as illumination changes, complex background and perspective change.
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- GLiT: Neural Architecture Search for Global and Local Image Transformer107
This work introduces the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition and introduces a locality module that models the local correlations in images explicitly with fewer computational cost.
- NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results106
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.
- Automatic polyp detection for wireless capsule endoscopy images106
This work attempts to design a computerized scheme aiming for polyp WCE image recognition though polyp in WCE images show great variations in appearance, which integrates advantages of wavelet transform and uniform local binary pattern.
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- AIM 2020 Challenge on Learned Image Signal Processing Pipeline90
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
- MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation87
A MetaCorrection framework is advanced, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation and the proposed DMLC strategy is employed to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation.
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- Computer-aided small bowel tumor detection for capsule endoscopy77
A new computer-aided detection scheme using color texture features and classifier ensemble is promising for small bowel tumor detection in capsule endoscopy images.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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