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Found 8 papers, 8 papers with code
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Therefore, we present a new LLVM, Mixture of All Intelligence (MoAI), which leverages auxiliary visual information obtained from the outputs of external segmentation, detection, SGG, and OCR models.
Ranked #27 on
Visual Question Answering
on MM-Vet
CoLLaVO: Crayon Large Language and Vision mOdel
Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on vision language (VL) tasks.
Ranked #35 on
Visual Question Answering
on MM-Vet
Causal Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations.
Ranked #1 on
Unsupervised Semantic Segmentation
on COCO-Stuff-81
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning
Adversarial examples derived from deliberately crafted perturbations on visual inputs can easily harm decision process of deep neural networks.
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression
The origin of adversarial examples is still inexplicable in research fields, and it arouses arguments from various viewpoints, albeit comprehensive investigations.
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Adversarial examples provoke weak reliability and potential security issues in deep neural networks.
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Adversarial examples, generated by carefully crafted perturbation, have attracted considerable attention in research fields.
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense.
