1.9k citations · 4.2k across the 30 of their papers we have counts for
18 papers · 1 filter
A Decade's Battle on Dataset Bias: Are We There Yet?
Zhuang Liu, Kaiming He
We revisit the "dataset classification" experiment suggested by Torralba & Efros (2011) a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets a…
Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Xinlei Chen, Zhuang Liu, Saining Xie +1
In this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconst…
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
Shengbang Tong, Zhuang Liu, Yuexiang Zhai +3
Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However, the visu…
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
Kirill Vishniakov, Zhiqiang Shen, Zhuang Liu
Modern computer vision offers a great variety of models to practitioners, and selecting a model from multiple options for specific applications can be challenging. Conventionally,…
ImageBind: One Embedding Space To Bind Them All
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu +4
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of p…
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu +4
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2…
A ConvNet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu +3
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification mo…
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space
Arnav Chavan, Zhiqiang Shen, Zhuang Liu +3
This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search…
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
John Lambert, Zhuang Liu, Ozan Sener +2
We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to in…
Anytime Dense Prediction with Confidence Adaptivity
Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang +2
Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image…
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu +3
The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views…
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Yinbo Chen, Zhuang Liu, Huijuan Xu +2
Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to…
Exploring Simple and Transferable Recognition-Aware Image Processing
Zhuang Liu, Hung-Ju Wang, Tinghui Zhou +4
Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans b…
Few-shot Object Detection via Feature Reweighting
Bingyi Kang, Zhuang Liu, Xin Wang +3
Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop…
Object Detection from Scratch with Deep Supervision
Zhiqiang Shen, Zhuang Liu, Jianguo Li +3
We propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-…
Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen +3
The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel…
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Zhiqiang Shen, Zhuang Liu, Jianguo Li +3
We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off-the-shelf n…
Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens van der Maaten +1
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the…