1 code implementation • 1 May 2024 • Bo Li, Haoke Xiao, Lv Tang
In the evolving landscape of computer vision, foundation models have emerged as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks.
no code implementations • 19 Nov 2023 • Lv Tang, Peng-Tao Jiang, Zhihao Shen, Hao Zhang, Jinwei Chen, Bo Li
Large Vision-Language Model (LVLM) has seen burgeoning development and increasing attention recently.
no code implementations • 17 Oct 2023 • Lv Tang, Peng-Tao Jiang, Hao-Ke Xiao, Bo Li
The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing.
1 code implementation • 11 Sep 2023 • Haoke Xiao, Lv Tang, Bo Li, Zhiming Luo, Shaozi Li
Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.
1 code implementation • 10 Apr 2023 • Lv Tang, Haoke Xiao, Bo Li
In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation.
no code implementations • ICCV 2023 • Lv Tang, Xinfeng Zhang, Gai Zhang, Xiaoqi Ma
Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed.
no code implementations • 25 Sep 2022 • Bo Li, Lv Tang, Senyun Kuang, Mofei Song, Shouhong Ding
In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG).
1 code implementation • CVPR 2022 • Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, Shouhong Ding
We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain.
no code implementations • 25 Oct 2021 • Yijie Zhong, Bo Li, Lv Tang, Hao Tang, Shouhong Ding
With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values.
2 code implementations • ICCV 2021 • Lv Tang, Bo Li, Shouhong Ding, Mofei Song
As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions.
Ranked #10 on RGB Salient Object Detection on DAVIS-S
no code implementations • 30 Apr 2021 • Lv Tang, Bo Li
Co-Salient Object Detection (CoSOD) aims at simulating the human visual system to discover the common and salient objects from a group of relevant images.
no code implementations • 23 Sep 2020 • Lv Tang, Bo Li
First, in order to leverage the different advantages of low-level and high-level features, we propose a novel non-local cross-level attention (CLA), which can capture the long-range feature dependencies to enhance the distinction of complete salient object.