208 citations · 631 across the 22 of their papers we have counts for
22 papers
Text-Conditional JEPA for Learning Semantically Rich Visual Representations
Chen Huang, Xianhang Li, Vimal Thilak +2
Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature prediction. However with the inhe…
DUET: 2D Structured and Approximately Equivariant Representations
Xavier Suau, Federico Danieli, T. Anderson Keller +5
Multiview Self-Supervised Learning (MSSL) is based on learning invariances with respect to a set of input transformations. However, invariance partially or totally removes transfor…
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch
Yuhang Zang, Kaiyang Zhou, Chen Huang +1
This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We prop…
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors
Chen Huang, Hanlin Goh, Jiatao Gu +1
Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstr…
Unified Vision and Language Prompt Learning
Yuhang Zang, Wei Li, Kaiyang Zhou +2
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision co…
Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram +7
Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of thei…
Efficient Representation Learning via Adaptive Context Pooling
Chen Huang, Walter Talbott, Navdeep Jaitly +1
Self-attention mechanisms model long-range context by using pairwise attention between all input tokens. In doing so, they assume a fixed attention granularity defined by the indiv…
Open-Vocabulary DETR with Conditional Matching
Yuhang Zang, Wei Li, Kaiyang Zhou +2
Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ide…
An Attention Free Transformer
Shuangfei Zhai, Walter Talbott, Nitish Srivastava +4
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are…
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics
Chen Huang, Shuangfei Zhai, Pengsheng Guo +1
We study the problem of directly optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in…
FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
Yuhang Zang, Chen Huang, Chen Change Loy
Recent methods for long-tailed instance segmentation still struggle on rare object classes with few training data. We propose a simple yet effective method, Feature Augmentation an…
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang, Shuangfei Zhai, Walter Talbott +4
In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this…
Dense Intrinsic Appearance Flow for Human Pose Transfer
Yining Li, Chen Huang, Chen Change Loy
We present a novel approach for the task of human pose transfer, which aims at synthesizing a new image of a person from an input image of that person and a target pose. We address…
Pose Guided Human Video Generation
Ceyuan Yang, Zhe Wang, Xinge Zhu +3
Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicit…
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Chen Huang, Yining Li, Chen Change Loy +1
Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of…
Mask-aware Photorealistic Face Attribute Manipulation
Ruoqi Sun, Chen Huang, Jianping Shi +1
The task of face attribute manipulation has found increasing applications, but still remains challenging with the requirement of editing the attributes of a face image while preser…
CNNs are Globally Optimal Given Multi-Layer Support
Chen Huang, Chen Kong, Simon Lucey
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs. Although giving impressive empirical performance it can be slow to converge. In this paper we e…
Learning Policies for Adaptive Tracking with Deep Feature Cascades
Chen Huang, Simon Lucey, Deva Ramanan
Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as w…
Learning to Disambiguate by Asking Discriminative Questions
Yining Li, Chen Huang, Xiaoou Tang +1
The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of genera…
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang +2
In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K…
Local Similarity-Aware Deep Feature Embedding
Chen Huang, Chen Change Loy, Xiaoou Tang
Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric. To make…
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
Chen Huang, Chen Change Loy, Xiaoou Tang
Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue conventional methods tend to be biased toward the majority class wit…