109 citations · 480 across the 17 of their papers we have counts for
19 papers
SVHalluc: Benchmarking Speech-Vision Hallucination in Audio-Visual Large Language Models
Chenshuang Zhang, Kyeong Seon Kim, Chengxin Liu +1
Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination. Existing benchmarks focus on environmenta…
Aligning What Vision-Language Models See and Perceive with Adaptive Information Flow
Chengxin Liu, Wonseok Choi, Chenshuang Zhang +1
Vision-Language Models (VLMs) have demonstrated strong capability in a wide range of tasks such as visual recognition, document parsing, and visual grounding. Nevertheless, recent…
Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
Chenshuang Zhang, Kang Zhang, Joon Son Chung +3
Distinguishing visually similar objects by their motion remains a critical challenge in computer vision. Although supervised trackers show promise, contemporary self-supervised tra…
Towards Understanding Dual BN In Hybrid Adversarial Training
Chenshuang Zhang, Chaoning Zhang, Kang Zhang +3
There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples…
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
Chenshuang Zhang, Fei Pan, Junmo Kim +2
We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of evaluation over s…
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,…
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…
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…
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…
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…
Text-to-image Diffusion Models in Generative AI: A Survey
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang +2
This survey reviews the progress of diffusion models in generating images from text, ~\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts wit…
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
Chaoning Zhang, Chenshuang Zhang, Junha Song +3
Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar tr…
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +4
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating…
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +3
To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving…
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign
Axi Niu, Kang Zhang, Chaoning Zhang +4
PGD-based and FGSM-based are two popular adversarial training (AT) approaches for obtaining adversarially robust models. Compared with PGD-based AT, FGSM-based one is significantly…
EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search
Wenjin Wu, Guojun Liu, Hui Ye +5
E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search sy…