25 citations · 51 across the 15 of their papers we have counts for
15 papers
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation
Anlin Zheng, Xin Wen, Xuanyang Zhang +5
In this work, we present a novel direction to build an image tokenizer directly on top of a frozen vision foundation model, which is a largely underexplored area. Specifically, we…
A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
Xin Wen, Bingchen Zhao, Yilun Chen +2
Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. Through systematic evaluation, we find that while DINO and iBO…
"Principal Components" Enable A New Language of Images
Xin Wen, Bingchen Zhao, Ismail Elezi +2
We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space. While existing visual tokenizers primarily optimize for re…
Can OOD Object Detectors Learn from Foundation Models?
Jiahui Liu, Xin Wen, Shizhen Zhao +2
Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such a…
What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights
Xin Wen, Bingchen Zhao, Yilun Chen +2
Severe data imbalance naturally exists among web-scale vision-language datasets. Despite this, we find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance…
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness
Jiequan Cui, Beier Zhu, Xin Wen +3
In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate tha…
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Chuofan Ma, Yi Jiang, Xin Wen +2
Deriving reliable region-word alignment from image-text pairs is critical to learn object-level vision-language representations for open-vocabulary object detection. Existing metho…
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
Letian Zhang, Xiaotong Zhai, Zhongkai Zhao +3
Counterfactual reasoning, a fundamental aspect of human cognition, involves contemplating alternatives to established facts or past events, significantly enhancing our abilities in…
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
Xiaoyang Wu, Zhuotao Tian, Xin Wen +4
The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep l…
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
Bingchen Zhao, Xin Wen, Kai Han
In this paper, we address the problem of generalized category discovery (GCD), \ie, given a set of images where part of them are labelled and the rest are not, the task is to autom…
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
Xiaoyang Wu, Xin Wen, Xihui Liu +1
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on vario…
Parametric Classification for Generalized Category Discovery: A Baseline Study
Xin Wen, Bingchen Zhao, Xiaojuan Qi
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric…
Self-Supervised Visual Representation Learning with Semantic Grouping
Xin Wen, Bingchen Zhao, Anlin Zheng +2
In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlyin…
Temporal Context Aggregation for Video Retrieval with Contrastive Learning
Jie Shao, Xin Wen, Bingchen Zhao +1
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events…
Distilling Visual Priors from Self-Supervised Learning
Bingchen Zhao, Xin Wen
Convolutional Neural Networks (CNNs) are prone to overfit small training datasets. We present a novel two-phase pipeline that leverages self-supervised learning and knowledge disti…