305 citations · 813 across the 36 of their papers we have counts for
18 papers · 1 filter
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
Qing Li, Boqing Gong, Yin Cui +4
In this paper, we explore the possibility of building a unified foundation model that can be adapted to both vision-only and text-only tasks. Starting from BERT and ViT, we design…
An Informative Tracking Benchmark
Xin Li, Qiao Liu, Wenjie Pei +4
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making…
Exploring Temporal Granularity in Self-Supervised Video Representation Learning
Rui Qian, Yeqing Li, Liangzhe Yuan +7
This work presents a self-supervised learning framework named TeG to explore Temporal Granularity in learning video representations. In TeG, we sample a long clip from a video and…
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Liangzhe Yuan, Rui Qian, Yin Cui +5
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video r…
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun +5
Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object…
Learning Continuous Environment Fields via Implicit Functions
Xueting Li, Shalini De Mello, Xiaolong Wang +3
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that…
Correcting Face Distortion in Wide-Angle Videos
Wei-Sheng Lai, YiChang Shih, Chia-Kai Liang +1
Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background. Unfortunately, due to persp…
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien +1
Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms…
Video Frame Interpolation Transformer
Zhihao Shi, Xiangyu Xu, Xiaohong Liu +2
Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights…
Hierarchical Modular Network for Video Captioning
Hanhua Ye, Guorong Li, Yuankai Qi +3
Video captioning aims to generate natural language descriptions according to the content, where representation learning plays a crucial role. Existing methods are mainly developed…
Restormer: Efficient Transformer for High-Resolution Image Restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan +3
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoratio…
Burst Image Restoration and Enhancement
Akshay Dudhane, Syed Waqas Zamir, Salman Khan +2
Modern handheld devices can acquire burst image sequence in a quick succession. However, the individual acquired frames suffer from multiple degradations and are misaligned due to…
Learning Contrastive Representation for Semantic Correspondence
Taihong Xiao, Sifei Liu, Shalini De Mello +3
Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even f…
Self-Supervised Tracking via Target-Aware Data Synthesis
Xin Li, Wenjie Pei, Yaowei Wang +3
While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensi…
Incremental False Negative Detection for Contrastive Learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng +2
Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. Howeve…
Large-scale Unsupervised Semantic Segmentation
Shanghua Gao, Zhong-Yu Li, Ming-Hsuan Yang +3
Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale u…
Intriguing Properties of Vision Transformers
Muzammal Naseer, Kanchana Ranasinghe, Salman Khan +3
Vision transformers (ViT) have demonstrated impressive performance across various machine vision problems. These models are based on multi-head self-attention mechanisms that can f…
InfinityGAN: Towards Infinite-Pixel Image Synthesis
Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng +2
We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitraril…