SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement

1 code implementation • 10 Feb 2025 • Yuqi Lin, Hengjia Li, Wenqi Shao, Zheng Yang, Jun Zhao, Xiaofei He, Ping Luo, Kaipeng Zhang

In contrast to prior refinement techniques that are tailored to specific models or tasks in a close-world manner, we propose SAMRefiner, a universal and efficient approach by adapting SAM to the mask refinement task.

Semantic Segmentation

GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation

no code implementations • 27 Nov 2024 • Pengfei Zhou, Xiaopeng Peng, Jiajun Song, Chuanhao Li, Zhaopan Xu, Yue Yang, Ziyao Guo, Hao Zhang, Yuqi Lin, Yefei He, Lirui Zhao, Shuo Liu, Tianhua Li, Yuxuan Xie, Xiaojun Chang, Yu Qiao, Wenqi Shao, Kaipeng Zhang

While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to data size and diversity limitations.

Diversity multimodal generation +1

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

1 code implementation • 24 Apr 2024 • Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, Jiayi Lei, Quanfeng Lu, Runjian Chen, Peng Xu, Renrui Zhang, Haozhe Zhang, Peng Gao, Yali Wang, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi Shao

Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation.

Position: Towards Implicit Prompt For Text-To-Image Models

no code implementations • 4 Mar 2024 • Yue Yang, Yuqi Lin, Hong Liu, Wenqi Shao, Runjian Chen, Hailong Shang, Yu Wang, Yu Qiao, Kaipeng Zhang, Ping Luo

We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.

Position

UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation

no code implementations • 23 Jan 2024 • Hengjia Li, Yang Liu, Yuqi Lin, Zhanwei Zhang, Yibo Zhao, weihang Pan, Tu Zheng, Zheng Yang, Yuchun Jiang, Boxi Wu, Deng Cai

In this paper, we propose UniHDA, a \textbf{unified} and \textbf{versatile} framework for generative hybrid domain adaptation with multi-modal references from multiple domains.

Attribute Diversity +1

TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training

1 code implementation • 20 Dec 2023 • Yuqi Lin, Minghao Chen, Kaipeng Zhang, Hengjia Li, Mingming Li, Zheng Yang, Dongqin Lv, Binbin Lin, Haifeng Liu, Deng Cai

As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.

Classification Multi-Label Classification +4

Few-shot Hybrid Domain Adaptation of Image Generators

1 code implementation • 30 Oct 2023 • Hengjia Li, Yang Liu, Linxuan Xia, Yuqi Lin, Tu Zheng, Zheng Yang, Wenxiao Wang, Xiaohui Zhong, Xiaobo Ren, Xiaofei He

Concretely, the distance loss blends the attributes of all target domains by reducing the distances from generated images to all target subspaces.

Domain Adaptation Semantic Similarity +1

CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation

1 code implementation • CVPR 2023 • Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He

To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.

Segmentation Weakly supervised Semantic Segmentation +1

Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations

no code implementations • 6 Dec 2022 • Minghao Chen, Renbo Tu, Chenxi Huang, Yuqi Lin, Boxi Wu, Deng Cai

In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos.

Action Classification Contrastive Learning +4