WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition

1 code implementation • 22 Feb 2024 • Lianghui Zhu, Junwei Zhou, Yan Liu, Xin Hao, Wenyu Liu, Xinggang Wang

Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem.

object-detection Segmentation +2

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

6 code implementations • 17 Jan 2024 • Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang

The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to be the next-generation backbone for vision foundation models.

object-detection Object Detection +3

JudgeLM: Fine-tuned Large Language Models are Scalable Judges

1 code implementation • 26 Oct 2023 • Lianghui Zhu, Xinggang Wang, Xinlong Wang

To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks.

WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation

1 code implementation • 3 Apr 2023 • Lianghui Zhu, Yingyue Li, Jiemin Fang, Yan Liu, Hao Xin, Wenyu Liu, Xinggang Wang

Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects.

Weakly-supervised Learning Weakly supervised Semantic Segmentation +1