35 citations · 148 across the 17 of their papers we have counts for
15 papers · 1 filter
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Lijie Fan, Tianhong Li, Siyang Qin +6
Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-ima…
Learning Vision from Models Rivals Learning Vision from Data
Yonglong Tian, Lijie Fan, Kaifeng Chen +3
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dat…
Scaling Laws of Synthetic Images for Model Training ... for Now
Lijie Fan, Kaifeng Chen, Dilip Krishnan +3
Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting cu…
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
Yonglong Tian, Lijie Fan, Phillip Isola +2
We investigate the potential of learning visual representations using synthetic images generated by text-to-image models. This is a natural question in the light of the excellent p…
Improving CLIP Training with Language Rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola +2
Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CL…
Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
Mingyu Ding, Yikang Shen, Lijie Fan +5
Humans possess a versatile mechanism for extracting structured representations of our visual world. When looking at an image, we can decompose the scene into entities and their par…
Unsupervised Learning for Human Sensing Using Radio Signals
Tianhong Li, Lijie Fan, Yuan Yuan +1
There is a growing literature demonstrating the feasibility of using Radio Frequency (RF) signals to enable key computer vision tasks in the presence of occlusions and poor lightin…
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
Tianhong Li, Peng Cao, Yuan Yuan +5
Real-world data often exhibits long tail distributions with heavy class imbalance, where the majority classes can dominate the training process and alter the decision boundaries of…
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Lijie Fan, Sijia Liu, Pin-Yu Chen +2
Contrastive learning (CL) can learn generalizable feature representations and achieve the state-of-the-art performance of downstream tasks by finetuning a linear classifier on top…
In-Home Daily-Life Captioning Using Radio Signals
Lijie Fan, Tianhong Li, Yuan Yuan +1
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. Addressing this problem requires…
Learning Longterm Representations for Person Re-Identification Using Radio Signals
Lijie Fan, Tianhong Li, Rongyao Fang +3
Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. Existing ReID methods rely on images or videos collected using RGB cameras…
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Tianhong Li, Lijie Fan, Mingmin Zhao +2
Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research…
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Lijie Fan, Wenbing Huang, Chuang Gan +2
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
Lijie Fan, Wenbing Huang, Chuang Gan +3
Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks. To fill this gap, we propose TVN…
Spatiotemporal Networks for Video Emotion Recognition
Lijie Fan, Yunjie Ke
Our experiment adapts several popular deep learning methods as well as some traditional methods on the problem of video emotion recognition. In our experiment, we use the CNN-LSTM…