144 citations · 865 across the 49 of their papers we have counts for
25 papers · 1 filter
MobileSAMv2: Faster Segment Anything to Everything
Chaoning Zhang, Dongshen Han, Sheng Zheng +3
Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for…
Single Image Reflection Removal with Patch Reflectance Prior
Dongshen Han, Heechan Yoon, Hyukmin Kwon +4
Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and ref…
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Chaoning Zhang, Yu Qiao, Shehbaz Tariq +5
In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently,…
Toward a Deeper Understanding: RetNet Viewed through Convolution
Chenghao Li, Chaoning Zhang
The success of Vision Transformer (ViT) has been widely reported on a wide range of image recognition tasks. ViT can learn global dependencies superior to CNN, yet CNN's inherent l…
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation
Dongshen Han, Chaoning Zhang, Sheng Zheng +3
As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored. This works investigat…
Black-box Targeted Adversarial Attack on Segment Anything (SAM)
Sheng Zheng, Chaoning Zhang, Xinhong Hao
Deep recognition models are widely vulnerable to adversarial examples, which change the model output by adding quasi-imperceptible perturbation to the image input. Recently, Segmen…
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning
Thanh Nguyen, Trung Pham, Chaoning Zhang +3
Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role. However, the recent development of new non-CL frameworks has ach…
Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
Loc X. Nguyen, Ye Lin Tun, Yan Kyaw Tun +4
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication syst…
Internal-External Boundary Attention Fusion for Glass Surface Segmentation
Dongshen Han, Seungkyu Lee, Chaoning Zhang +4
Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of…
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Chaoning Zhang, Dongshen Han, Yu Qiao +4
Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like im…
Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions
Xinru Shan, Chaoning Zhang
Segment Anything Model (SAM) has gained considerable interest in recent times for its remarkable performance and has emerged as a foundational model in computer vision. It has been…
Segment Anything Meets Semantic Communication
Shehbaz Tariq, Brian Estadimas Arfeto, Chaoning Zhang +1
In light of the diminishing returns of traditional methods for enhancing transmission rates, the domain of semantic communication presents promising new frontiers. Focusing on imag…
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Yu Qiao, Chaoning Zhang, Taegoo Kang +3
Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance…
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
Chaoning Zhang, Joseph Cho, Fachrina Dewi Puspitasari +11
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentat…
When ChatGPT for Computer Vision Will Come? From 2D to 3D
Chenghao Li, Chaoning Zhang
ChatGPT and its improved variant GPT4 have revolutionized the NLP field with a single model solving almost all text related tasks. However, such a model for computer vision does no…
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples
Chenshuang Zhang, Chaoning Zhang, Taegoo Kang +3
Segment Anything Model (SAM) has attracted significant attention recently, due to its impressive performance on various downstream tasks in a zero-short manner. Computer vision (CV…
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Chenghao Li, Chaoning Zhang, Joseph Cho +6
Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions a…
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Dongsheng Han, Chaoning Zhang, Yu Qiao +5
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of…
What if we have Meta GPT? From Content Singularity to Human-Metaverse Interaction in AIGC Era
Lik-Hang Lee, Pengyuan Zhou, Chaoning Zhang +1
The global metaverse development is facing a "cooldown moment", while the academia and industry attention moves drastically from the Metaverse to AI Generated Content (AIGC) in 202…
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Chaoning Zhang, Chenshuang Zhang, Chenghao Li +13
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence…
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Mengchun Zhang, Maryam Qamar, Taegoo Kang +4
Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of a…
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI
Chenshuang Zhang, Chaoning Zhang, Sheng Zheng +4
Generative AI has demonstrated impressive performance in various fields, among which speech synthesis is an interesting direction. With the diffusion model as the most popular gene…
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
Yu Qiao, Md. Shirajum Munir, Apurba Adhikary +4
Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution…
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
Chaoning Zhang, Chenshuang Zhang, Sheng Zheng +14
As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With su…