Related references
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Article
Computer Science, Artificial Intelligence
Ziwei Wang et al.
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Article
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Article
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Article
Computer Science, Artificial Intelligence
Zhaowei Cai et al.
Summary: In object detection, the commonly used IoU threshold of 0.5 can lead to noisy detections, and performance may degrade for larger thresholds. The Cascade R-CNN architecture addresses this issue by training detectors sequentially with increasing IoU thresholds and eliminating quality mismatches at inference, resulting in state-of-the-art performance and significant improvement in high-quality detection across various datasets. The model is also generalized to instance segmentation, achieving nontrivial improvements over existing methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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Proceedings Paper
Computer Science, Artificial Intelligence
Ziwei Wang et al.
Summary: This paper introduces a generalizable mixed-precision quantization method, which significantly reduces search cost on large-scale datasets without performance degradation. By accurately locating network attribution across different data distributions, the method achieves a competitive accuracy-complexity trade-off.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
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Yi Guo et al.
Summary: Model compression techniques are gaining attention for obtaining efficient AI models, with channel pruning being an important strategy. Previous methods have limitations, but the GDP algorithm proposed in this study shows promising results in improving model performance.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
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Proceedings Paper
Computer Science, Artificial Intelligence
Boyu Chen et al.
Summary: The paper introduces a new Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition. By incorporating a locality module and new search algorithms, the method allows for a trade-off between global and local information, as well as optimizing low-level design choices in each module. Through extensive experiments on the ImageNet dataset, the method demonstrates the ability to find more efficient and discriminative transformer variants compared to existing models like ResNet101 and ViT.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhihe Lu et al.
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2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yehui Tang et al.
Summary: The proposed approach in this paper dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the method is verified on several benchmarks, showing better performance in terms of both accuracy and computational cost compared to state-of-the-art methods. This new paradigm maximally excavates redundancy in the network architecture.
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Proceedings Paper
Computer Science, Artificial Intelligence
Zhaowei Cai et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Hyeji Kim et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaiming He et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
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Proceedings Paper
Computer Science, Artificial Intelligence
Yihui He et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)