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Found 17 papers, 8 papers with code
Learning Vision from Models Rivals Learning Vision from Data
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data.
Scaling Laws of Synthetic Images for Model Training ... for Now
Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e. g., fewer than 0. 5 million images in ImageNet), (2) when the evaluation dataset diverges significantly from the training data, indicating the out-of-distribution scenario, or (3) when synthetic data is used in conjunction with real images, as demonstrated in the training of CLIP models.
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
We investigate the potential of learning visual representations using synthetic images generated by text-to-image models.
Improving CLIP Training with Language Rewrites
During training, LaCLIP randomly selects either the original texts or the rewritten versions as text augmentations for each image.
Reparo: Loss-Resilient Generative Codec for Video Conferencing
Packet loss during video conferencing often leads to poor quality and video freezing.
Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
When looking at an image, we can decompose the scene into entities and their parts as well as obtain the dependencies between them.
Unsupervised Learning for Human Sensing Using Radio Signals
Thus, in this paper, we explore the feasibility of adapting RGB-based unsupervised representation learning to RF signals.
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.
Ranked #22 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency.
Addressing Feature Suppression in Unsupervised Visual Representations
However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.
In-Home Daily-Life Captioning Using Radio Signals
This paper aims to caption daily life --i. e., to create a textual description of people's activities and interactions with objects in their homes.
Learning Longterm Representations for Person Re-Identification Using Radio Signals
RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape.
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing them.
Ranked #1 on
RF-based Pose Estimation
on RF-MMD
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip.
End-to-End Learning of Motion Representation for Video Understanding
Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.
Ranked #42 on
Action Recognition
on UCF101
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatio-temporal data in various disciplines.
Spatiotemporal Networks for Video Emotion Recognition
Our experiment adapts several popular deep learning methods as well as some traditional methods on the problem of video emotion recognition.
