36 citations · 59 across the 9 of their papers we have counts for
9 papers
Agent-based Video Trimming
Lingfeng Yang, Zhenyuan Chen, Xiang Li +3
As information becomes more accessible, user-generated videos are increasing in length, placing a burden on viewers to sift through vast content for valuable insights. This trend u…
Revisiting Prompt Pretraining of Vision-Language Models
Zhenyuan Chen, Lingfeng Yang, Shuo Chen +3
Prompt learning is an effective method to customize Vision-Language Models (VLMs) for various downstream tasks, involving tuning very few parameters of input prompt tokens. Recentl…
Add-SD: Rational Generation without Manual Reference
Lingfeng Yang, Xinyu Zhang, Xiang Li +7
Diffusion models have exhibited remarkable prowess in visual generalization. Building on this success, we introduce an instruction-based object addition pipeline, named Add-SD, whi…
Fine-Grained Visual Prompting
Lingfeng Yang, Yueze Wang, Xiang Li +2
Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited…
A Survey of Historical Learning: Learning Models with Learning History
Xiang Li, Ge Wu, Lingfeng Yang +3
New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this su…
Curriculum Temperature for Knowledge Distillation
Zheng Li, Xiang Li, Lingfeng Yang +5
Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid searc…
Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
Xiang Li, Wenhai Wang, Lingfeng Yang +1
Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-tra…
RecursiveMix: Mixed Learning with History
Lingfeng Yang, Xiang Li, Borui Zhao +2
Mix-based augmentation has been proven fundamental to the generalization of deep vision models. However, current augmentations only mix samples at the current data batch during tra…
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information
Lingfeng Yang, Xiang Li, Renjie Song +5
Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on vi…