325 citations · 1.7k across the 55 of their papers we have counts for
18 papers · 1 filter
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4
In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using label…
Test-Time Adaptation for Visual Document Understanding
Sayna Ebrahimi, Sercan O. Arik, Tomas Pfister
For visual document understanding (VDU), self-supervised pretraining has been shown to successfully generate transferable representations, yet, effective adaptation of such represe…
Prefix Conditioning Unifies Language and Label Supervision
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4
Image-classification datasets have been used to pretrain image recognition models. Recently, web-scale image-caption datasets have emerged as a source of powerful pretraining alter…
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li +3
We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conduc…
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li +2
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike…
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding
Zizhao Zhang, Han Zhang, Long Zhao +3
Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea…
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon +1
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage…
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
Kunpeng Li, Zizhao Zhang, Guanhang Wu +5
Learning visual knowledge from massive weakly-labeled web videos has attracted growing research interests thanks to the large corpus of easily accessible video data on the Internet…
Learning and Evaluating Representations for Deep One-class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon +2
We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on lear…
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang +4
Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accu…
A Simple Semi-Supervised Learning Framework for Object Detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li +3
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data. Although there has been remarkable recent prog…
Generative Modeling for Small-Data Object Detection
Lanlan Liu, Michael Muelly, Jia Deng +2
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This…
Inserting Videos into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang
In this paper, we introduce a new problem of manipulating a given video by inserting other videos into it. Our main task is, given an object video and a scene video, to insert the…
Harmonic Unpaired Image-to-image Translation
Rui Zhang, Tomas Pfister, Jia Li
The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, bu…
Learning from Simulated and Unsupervised Images through Adversarial Training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel +3
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from…
Personalizing Human Video Pose Estimation
James Charles, Tomas Pfister, Derek Magee +2
We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the…
Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods
Xiaobai Li, Xiaopeng Hong, Antti Moilanen +4
Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many imp…
Flowing ConvNets for Human Pose Estimation in Videos
Tomas Pfister, James Charles, Andrew Zisserman
The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal cont…