Search Results for author: Ge-Peng Ji

Found 26 papers, 22 papers with code

Effectiveness Assessment of Recent Large Vision-Language Models

no code implementations7 Mar 2024 Yao Jiang, Xinyu Yan, Ge-Peng Ji, Keren Fu, Meijun Sun, Huan Xiong, Deng-Ping Fan, Fahad Shahbaz Khan

The advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence.

Anomaly Detection Attribute +7

Large Model Based Referring Camouflaged Object Detection

no code implementations28 Nov 2023 Shupeng Cheng, Ge-Peng Ji, Pengda Qin, Deng-Ping Fan, BoWen Zhou, Peng Xu

Our motivation is to make full use of the semantic intelligence and intrinsic knowledge of recent Multimodal Large Language Models (MLLMs) to decompose this complex task in a human-like way.

Object object-detection +2

How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

1 code implementation27 Jul 2023 Haotong Qin, Ge-Peng Ji, Salman Khan, Deng-Ping Fan, Fahad Shahbaz Khan, Luc van Gool

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI.

Rethinking Polyp Segmentation from an Out-of-Distribution Perspective

1 code implementation13 Jun 2023 Ge-Peng Ji, Jing Zhang, Dylan Campbell, Huan Xiong, Nick Barnes

Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.

Segmentation Self-Supervised Learning

Advances in Deep Concealed Scene Understanding

1 code implementation21 Apr 2023 Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, Luc van Gool

Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage.

Scene Understanding Semantic Segmentation

SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything"

no code implementations12 Apr 2023 Ge-Peng Ji, Deng-Ping Fan, Peng Xu, Ming-Ming Cheng, BoWen Zhou, Luc van Gool

Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision.

Masked Vision-Language Transformer in Fashion

1 code implementation27 Oct 2022 Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao, Deng-Ping Fan, Christos Sakaridis, Luc van Gool

We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation.

Image Reconstruction Retrieval

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection

1 code implementation8 Aug 2022 Wenbo Zhang, Keren Fu, Zhuo Wang, Ge-Peng Ji, Qijun Zhao

Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality.

object-detection RGB-D Salient Object Detection +2

Camouflaged Object Detection via Context-aware Cross-level Fusion

2 code implementations27 Jul 2022 Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, Tao Zhou

To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects.

object-detection Object Detection

Deep Gradient Learning for Efficient Camouflaged Object Detection

1 code implementation25 May 2022 Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc van Gool

This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).

Defect Detection Object +4

Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

1 code implementation5 Nov 2021 Ge-Peng Ji, Lei Zhu, Mingchen Zhuge, Keren Fu

Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e. g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest.

Image Segmentation Medical Image Segmentation +3

Full-Duplex Strategy for Video Object Segmentation

1 code implementation ICCV 2021 Ge-Peng Ji, Deng-Ping Fan, Keren Fu, Zhe Wu, Jianbing Shen, Ling Shao

Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues.

Object Salient Object Detection +6

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection

1 code implementation5 Jul 2021 Wenbo Zhang, Ge-Peng Ji, Zhuo Wang, Keren Fu, Qijun Zhao

To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.

object-detection RGB-D Salient Object Detection +1

Guidance and Teaching Network for Video Salient Object Detection

no code implementations21 May 2021 Yingxia Jiao, Xiao Wang, Yu-Cheng Chou, Shouyuan Yang, Ge-Peng Ji, Rong Zhu, Ge Gao

Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.

Object object-detection +2

Camouflaged Object Segmentation with Distraction Mining

1 code implementation CVPR 2021 Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan

In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature.

Camouflaged Object Segmentation Dichotomous Image Segmentation +3

Concealed Object Detection

1 code implementation20 Feb 2021 Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.

Camouflaged Object Segmentation Dichotomous Image Segmentation +2

Light Field Salient Object Detection: A Review and Benchmark

1 code implementation10 Oct 2020 Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, Deng-Ping Fan

Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.

Benchmarking Object +4

Siamese Network for RGB-D Salient Object Detection and Beyond

2 code implementations26 Aug 2020 Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu

Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.

object-detection RGB-D Salient Object Detection +2

Re-thinking Co-Salient Object Detection

2 code implementations7 Jul 2020 Deng-Ping Fan, Tengpeng Li, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Ming-Ming Cheng, Huazhu Fu, Jianbing Shen

CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.

Benchmarking Co-Salient Object Detection +3

Cannot find the paper you are looking for? You can Submit a new open access paper.