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Found 15 papers, 7 papers with code
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation.
Learning Multimodal Data Augmentation in Feature Space
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems.
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
Multimodal image-text models have shown remarkable performance in the past few years.
Visual Prompt Tuning for Test-time Domain Adaptation
First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.
Ranked #3 on
Domain Adaptation
on VisDA2017
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.
CrossNorm and SelfNorm for Generalization under Distribution Shifts
Can we develop new normalization methods to improve generalization robustness under distribution shifts?
Unity of Opposites: SelfNorm and CrossNorm for Model Robustness
CrossNorm exchanges styles between feature channels to perform style augmentation, diversifying the content and style mixtures.
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
First is that most if not all modern augmentation search methods are offline and learning policies are isolated from their usage.
AdaTransform: Adaptive Data Transformation
The AdaTransform can increase data variance in training and decrease data variance in testing.
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.
CU-Net: Coupled U-Nets
We design a new connectivity pattern for the U-Net architecture.
Ranked #30 on
Pose Estimation
on MPII Human Pose
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.
Ranked #19 on
Pose Estimation
on MPII Human Pose
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Random data augmentation is a critical technique to avoid overfitting in training deep neural network models.
Ranked #3 on
Pose Estimation
on Leeds Sports Poses
Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.
Vision-based Robotic Arm Imitation by Human Gesture
One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate.
Robotics
