214 citations · 638 across the 34 of their papers we have counts for
34 papers · 1 filter
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks
Jindong Gu, Fangyun Wei, Philip Torr +1
Deep Neural Networks can be easily fooled by small and imperceptible perturbations. The query-based black-box attack (QBBA) is able to create the perturbations using model output p…
Improving Continuous Sign Language Recognition with Cross-Lingual Signs
Fangyun Wei, Yutong Chen
This work dedicates to continuous sign language recognition (CSLR), which is a weakly supervised task dealing with the recognition of continuous signs from videos, without any prio…
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
Yiting Cheng, Fangyun Wei, Jianmin Bao +2
This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) r…
Side Adapter Network for Open-Vocabulary Semantic Segmentation
Mengde Xu, Zheng Zhang, Fangyun Wei +2
This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN). Our approach models the…
Natural Language-Assisted Sign Language Recognition
Ronglai Zuo, Fangyun Wei, Brian Mak
Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations…
Two-shot Video Object Segmentation
Kun Yan, Xiao Li, Fangyun Wei +4
Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In th…
DeepMIM: Deep Supervision for Masked Image Modeling
Sucheng Ren, Fangyun Wei, Samuel Albanie +2
Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since i…
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Sucheng Ren, Fangyun Wei, Zheng Zhang +1
Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only m…
Attentive Mask CLIP
Yifan Yang, Weiquan Huang, Yixuan Wei +8
Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adverse…
Two-Stream Network for Sign Language Recognition and Translation
Yutong Chen, Ronglai Zuo, Fangyun Wei +3
Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existin…
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
Yue Wu, Yu Deng, Jiaolong Yang +3
Although 2D generative models have made great progress in face image generation and animation, they often suffer from undesirable artifacts such as 3D inconsistency when rendering…
Conditional DETR V2: Efficient Detection Transformer with Box Queries
Xiaokang Chen, Fangyun Wei, Gang Zeng +1
In this paper, we are interested in Detection Transformer (DETR), an end-to-end object detection approach based on a transformer encoder-decoder architecture without hand-crafted p…
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Yu Du, Miaojing Shi, Fangyun Wei +1
Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training set. One representative solution is to directly learn an embedding function associating…
Unsupervised Prompt Learning for Vision-Language Models
Tony Huang, Jack Chu, Fangyun Wei
Contrastive vision-language models like CLIP have shown great progress in transfer learning. In the inference stage, the proper text description, also known as prompt, needs to be…
Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning
Minghao Chen, Fangyun Wei, Chong Li +1
Prior works on action representation learning mainly focus on designing various architectures to extract the global representations for short video clips. In contrast, many practic…
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Yu Du, Fangyun Wei, Zihe Zhang +3
Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The…
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
Yutong Chen, Fangyun Wei, Xiao Sun +2
This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pa…
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
Mengde Xu, Zheng Zhang, Fangyun Wei +4
Recently, open-vocabulary image classification by vision language pre-training has demonstrated incredible achievements, that the model can classify arbitrary categories without se…
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Yinghao Xu, Fangyun Wei, Xiao Sun +5
Semi-supervised action recognition is a challenging but important task due to the high cost of data annotation. A common approach to this problem is to assign unlabeled data with p…
Towards Tokenized Human Dynamics Representation
Kenneth Li, Xiao Sun, Zhirong Wu +2
For human action understanding, a popular research direction is to analyze short video clips with unambiguous semantic content, such as jumping and drinking. However, methods for u…
Bootstrap Your Object Detector via Mixed Training
Mengde Xu, Zheng Zhang, Fangyun Wei +5
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free. MixTraining enhances data augmentation by ut…
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
Hanzhe Hu, Fangyun Wei, Han Hu +3
Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes…
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
Yangyu Huang, Hao Yang, Chong Li +2
The recent progress of CNN has dramatically improved face alignment performance. However, few works have paid attention to the error-bias with respect to error distribution of faci…
Dual Path Learning for Domain Adaptation of Semantic Segmentation
Yiting Cheng, Fangyun Wei, Jianmin Bao +3
Domain adaptation for semantic segmentation enables to alleviate the need for large-scale pixel-wise annotations. Recently, self-supervised learning (SSL) with a combination of ima…