1 citations · 4 across the 9 of their papers we have counts for
10 papers
Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets
Kaifeng Chen, Lechao Cheng, Jiyang Li +6
Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervision, DD risks condensing cor…
Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
Dror Aiger, Bingyi Cao, Kaifeng Chen +1
The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching…
Infusing fine-grained visual knowledge to Vision-Language Models
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araujo +1
Large-scale contrastive pre-training produces powerful Vision-and-Language Models (VLMs) capable of generating representations (embeddings) effective for a wide variety of visual a…
Dataset Distillers Are Good Label Denoisers In the Wild
Lechao Cheng, Kaifeng Chen, Jiyang Li +3
Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then ap…
TIPS: Text-Image Pretraining with Spatial awareness
Kevis-Kokitsi Maninis, Kaifeng Chen, Soham Ghosh +11
While image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense und…
UDON: Universal Dynamic Online distillatioN for generic image representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araujo +1
Universal image representations are critical in enabling real-world fine-grained and instance-level recognition applications, where objects and entities from any domain must be ide…
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…
Improve Supervised Representation Learning with Masked Image Modeling
Kaifeng Chen, Daniel Salz, Huiwen Chang +3
Training visual embeddings with labeled data supervision has been the de facto setup for representation learning in computer vision. Inspired by recent success of adopting masked i…
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, Bingyi Cao +7
Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractica…