Search Results for author:
Found 10 papers, 6 papers with code
SeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Learning
To unleash the potential of fixed deep features, we propose a novel semantic adversarial augmentation (SeA) in the feature space for optimization.
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github. com/X-PLUG/mPLUG-Owl.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
(Question Pair Acc metric)
Visual Question Answering (VQA)
Zero-Shot Video Question Answer
Improved Visual Fine-tuning with Natural Language Supervision
Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with limited training examples.
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
Ranked #1 on
Video Captioning
on MSR-VTT
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation
With our empirical result obtained from 1, 330 models, we provide the following main observations: 1) ERM combined with data augmentation can achieve state-of-the-art performance if we choose a proper pre-trained model respecting the data property; 2) specialized algorithms further improve the robustness on top of ERM when handling a specific type of distribution shift, e. g., GroupDRO for spurious correlation and CORAL for large-scale out-of-distribution data; 3) Comparing different pre-training modes, architectures and data sizes, we provide novel observations about pre-training on distribution shift, which sheds light on designing or selecting pre-training strategy for different kinds of distribution shifts.
Improved Fine-Tuning by Better Leveraging Pre-Training Data
The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning.
Unsupervised Visual Representation Learning by Online Constrained K-Means
Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
Ranked #6 on
Unsupervised Image Classification
on CIFAR-10
Towards Understanding Label Smoothing
Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants.
Weakly Supervised Representation Learning with Coarse Labels
To mitigate this challenge, we propose an algorithm to learn the fine-grained patterns for the target task, when only its coarse-class labels are available.
Combining SLAM with muti-spectral photometric stereo for real-time dense 3D reconstruction
Obtaining dense 3D reconstrution with low computational cost is one of the important goals in the field of SLAM.
