2 code implementations • 11 Apr 2024 • Bohao Peng, Zhuotao Tian, Shu Liu, MingChang Yang, Jiaya Jia
In this study, we introduce the Scalable Language Model (SLM) to overcome these limitations within a more challenging and generalized setting, representing a significant advancement toward practical applications for continual learning.
1 code implementation • 21 Mar 2024 • Bohao Peng, Xiaoyang Wu, Li Jiang, Yukang Chen, Hengshuang Zhao, Zhuotao Tian, Jiaya Jia
This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost.
Ranked #5 on 3D Semantic Segmentation on SemanticKITTI (val mIoU metric)
1 code implementation • 14 Mar 2024 • Chengyao Wang, Li Jiang, Xiaoyang Wu, Zhuotao Tian, Bohao Peng, Hengshuang Zhao, Jiaya Jia
To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning.
no code implementations • 28 Dec 2023 • Senqiao Yang, Tianyuan Qu, Xin Lai, Zhuotao Tian, Bohao Peng, Shu Liu, Jiaya Jia
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats.
1 code implementation • 7 Dec 2023 • Yuechen Zhang, Shengju Qian, Bohao Peng, Shu Liu, Jiaya Jia
Without tuning on LLaVA-v1. 5, our method secured 70. 7 in the MMBench test and 1552. 5 in MME-perception.
1 code implementation • 18 Aug 2023 • Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao
In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets.
Ranked #3 on 3D Semantic Segmentation on SemanticKITTI (val mIoU metric, using extra training data)
no code implementations • 27 Jun 2023 • Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chengyao Wang, Shu Liu, Jingyong Su, Jiaya Jia
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
1 code implementation • CVPR 2023 • Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chenyao Wang, Shu Liu, Jingyong Su, Jiaya Jia
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #6 on Few-Shot Semantic Segmentation on COCO-20i (1-shot)