8 citations · 32 across the 32 of their papers we have counts for
11 papers · 1 filter
Treat Visual Tokens as Text? But Your MLLM Only Needs Fewer Efforts to See
Zeliang Zhang, Phu Pham, Wentian Zhao +6
By treating visual tokens from visual encoders as text tokens, Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse visual understanding tasks,…
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
Yolo Y. Tang, Junjia Guo, Hang Hua +9
The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However…
Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?
Zeliang Zhang, Xin Liang, Mingqian Feng +2
As the demand for high-quality training data escalates, researchers have increasingly turned to generative models to create synthetic data, addressing data scarcity and enabling co…
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP
Zeliang Zhang, Zhuo Liu, Mingqian Feng +1
CLIP has demonstrated great versatility in adapting to various downstream tasks, such as image editing and generation, visual question answering, and video understanding. However,…
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
Zeliang Zhang, Xiaodong Liu, Hao Cheng +2
By increasing model parameters but activating them sparsely when performing a task, the use of Mixture-of-Experts (MoE) architecture significantly improves the performance of Large…
Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning?
Mingqian Feng, Yunlong Tang, Zeliang Zhang +1
Large Vision-Language Models (LVLMs) excel in integrating visual and linguistic contexts to produce detailed content, facilitating applications such as image captioning. However, u…
Learning to Transform Dynamically for Better Adversarial Transferability
Rongyi Zhu, Zeliang Zhang, Susan Liang +2
Adversarial examples, crafted by adding perturbations imperceptible to humans, can deceive neural networks. Recent studies identify the adversarial transferability across various m…
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
Zeliang Zhang, Mingqian Feng, Zhiheng Li +1
Machine learning models can perform well on in-distribution data but often fail on biased subgroups that are underrepresented in the training data, hindering the robustness of mode…
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
Zeliang Zhang, Jinyang Jiang, Zhuo Liu +3
Efficient and biologically plausible alternatives to backpropagation in neural network training remain a challenge due to issues such as high computational complexity and additiona…
Forward Learning for Gradient-based Black-box Saliency Map Generation
Zeliang Zhang, Mingqian Feng, Jinyang Jiang +3
Gradient-based saliency maps are widely used to explain deep neural network decisions. However, as models become deeper and more black-box, such as in closed-source APIs like ChatG…
Bag of Tricks to Boost Adversarial Transferability
Zeliang Zhang, Wei Yao, Xiaosen Wang
Deep neural networks are widely known to be vulnerable to adversarial examples. However, vanilla adversarial examples generated under the white-box setting often exhibit low transf…