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20192026
most citedRethinking the Route Towards Weakly Supervised Object Localization

7 citations · 14 across the 8 of their papers we have counts for

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cs.CV2026

Rethinking Genomic Modeling Through Optical Character Recognition

Hongxin Xiang, Pengsen Ma, Yunkang Cao +4

Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is stru…

cs.CV2024

On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning

Yun-Hao Cao, Jianxin Wu

Self-supervised learning (SSL) has developed rapidly in recent years. However, most of the mainstream methods are computationally expensive and rely on two (or more) augmentations…

cs.CV2023★ 1 cited

Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning

Yun-Hao Cao, Peiqin Sun, Shuchang Zhou

We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different de…

cs.CV2022★ 1 cited

Synergistic Self-supervised and Quantization Learning

Yun-Hao Cao, Peiqin Sun, Yechang Huang +2

With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream t…

cs.CV2022★ 2 cited

Worst Case Matters for Few-Shot Recognition

Minghao Fu, Yun-Hao Cao, Jianxin Wu

Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over…

cs.CV2022★ 3 cited

Training Vision Transformers with Only 2040 Images

Yun-Hao Cao, Hao Yu, Jianxin Wu

Vision Transformers (ViTs) is emerging as an alternative to convolutional neural networks (CNNs) for visual recognition. They achieve competitive results with CNNs but the lack of…

cs.CV2020★ 7 cited

Rethinking the Route Towards Weakly Supervised Object Localization

Chen-Lin Zhang, Yun-Hao Cao, Jianxin Wu

Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to…