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Jinghao Zhou

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3 papers

2 author rows

ICLR Conference 2024 Conference Paper

Exploring Target Representations for Masked Autoencoders

Xingbin Liu
Jinghao Zhou
Tao Kong
Xianming Lin
Rongrong Ji

Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to assigned target representations. In this paper, we show that a careful choice of the target representation is unnecessary for learning good visual representation since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any effort to carefully design the target representation. On various downstream tasks, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dbot) outperforms previous self-supervised methods by nontrivial margins. We hope our findings, as well as the proposed method, could motivate people to rethink the roles of target representations in pre-training masked autoencoders.

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ICLR Conference 2022 Conference Paper

Image BERT Pre-training with Online Tokenizer

Jinghao Zhou
Chen Wei 0005
Huiyu Wang
Wei Shen 0002
Cihang Xie
Alan L. Yuille
Tao Kong

The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces. In this work, we study masked image modeling (MIM) and indicate the necessity and challenges of using a semantically meaningful visual tokenizer. We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer. Specifically, we perform self-distillation on masked patch tokens and take the teacher network as the online tokenizer, along with self-distillation on the class token to acquire visual semantics. The online tokenizer is jointly learnable with the MIM objective and dispenses with a multi-stage training pipeline where the tokenizer needs to be pre-trained beforehand. We show the prominence of iBOT by achieving an 82.3% linear probing accuracy and an 87.8% fine-tuning accuracy evaluated on ImageNet-1K. Beyond the state-of-the-art image classification results, we underline emerging local semantic patterns, which helps the models to obtain strong robustness against common corruptions and achieve leading results on dense downstream tasks, e.g., object detection, instance segmentation, and semantic segmentation.

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AAAI Conference 2020 Conference Paper

Discriminative and Robust Online Learning for Siamese Visual Tracking

Jinghao Zhou
Peng Wang
Haoyang Sun

The problem of visual object tracking has traditionally been handled by variant tracking paradigms, either learning a model of the object’s appearance exclusively online or matching the object with the target in an ofﬂine-trained embedding space. Despite the recent success, each method agonizes over its intrinsic constraint. The online-only approaches suffer from a lack of generalization of the model they learn thus are inferior in target regression, while the ofﬂine-only approaches (e. g. , convolutional siamese trackers) lack the target-speciﬁc context information thus are not discriminative enough to handle distractors, and robust enough to deformation. Therefore, we propose an online module with an attention mechanism for ofﬂine siamese networks to extract target-speciﬁc features under L2 error. We further propose a ﬁlter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning. Effectiveness can be validated in the consistent improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask. Beyond that, our model based on SiamRPN++ obtains the best results over six popular tracking benchmarks and can operate beyond real-time.

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