224 citations · 2.3k across the 117 of their papers we have counts for
22 papers · 1 filter
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…
MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection
Yongri Piao, Jian Wang, Miao Zhang +1
Weakly supervised salient object detection (WSOD) targets to train a CNNs-based saliency network using only low-cost annotations. Existing WSOD methods take various techniques to p…
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
Youwei Pang, Xiaoqi Zhao, Lihe Zhang +1
Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cro…
Transformer-based Network for RGB-D Saliency Detection
Yue Wang, Xu Jia, Lu Zhang +3
RGB-D saliency detection integrates information from both RGB images and depth maps to improve prediction of salient regions under challenging conditions. The key to RGB-D saliency…
MODNet-V: Improving Portrait Video Matting via Background Restoration
Jiayu Sun, Zhanghan Ke, Lihe Zhang +2
To address the challenging portrait video matting problem more precisely, existing works typically apply some matting priors that require additional user efforts to obtain, such as…
To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection
Yongri Piao, Jian Wang, Miao Zhang +2
Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. Despite of the success of previous works, explorations on an effect…
Automatic Polyp Segmentation via Multi-scale Subtraction Network
Xiaoqi Zhao, Lihe Zhang, Huchuan Lu
More than 90\% of colorectal cancer is gradually transformed from colorectal polyps. In clinical practice, precise polyp segmentation provides important information in the early de…
Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation
Xiaoqi Zhao, Youwei Pang, Jiaxing Yang +2
Location and appearance are the key cues for video object segmentation. Many sources such as RGB, depth, optical flow and static saliency can provide useful information about the o…
Video Annotation for Visual Tracking via Selection and Refinement
Kenan Dai, Jie Zhao, Lijun Wang +5
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve. We pr…
HAT: Hierarchical Aggregation Transformers for Person Re-identification
Guowen Zhang, Pingping Zhang, Jinqing Qi +1
Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. However, with limited…
Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen +5
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However…
Animatable Neural Radiance Fields from Monocular RGB Videos
Jianchuan Chen, Ying Zhang, Di Kang +4
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos. Our approach extends neural radiance fields (NeRF) to the d…
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…
Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation
Guang Feng, Zhiwei Hu, Lihe Zhang +1
Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the netw…
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Bin Yan, Houwen Peng, Kan Wu +3
Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployment…
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +3
Video-based person re-identification (Re-ID) aims to retrieve video sequences of the same person under non-overlapping cameras. Previous methods usually focus on limited views, suc…
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +2
Video-based person re-identification (Re-ID) aims to automatically retrieve video sequences of the same person under non-overlapping cameras. To achieve this goal, it is the key to…
Learning Spatio-Temporal Transformer for Visual Tracking
Bin Yan, Houwen Peng, Jianlong Fu +2
In this paper, we present a new tracking architecture with an encoder-decoder transformer as the key component. The encoder models the global spatio-temporal feature dependencies b…
Transformer Tracking
Xin Chen, Bin Yan, Jiawen Zhu +3
Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the…
Similarity Reasoning and Filtration for Image-Text Matching
Haiwen Diao, Ying Zhang, Lin Ma +1
Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or lo…
Self-Supervised Pretraining for RGB-D Salient Object Detection
Xiaoqi Zhao, Youwei Pang, Lihe Zhang +2
Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initi…
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
Tao Huang, Songjiang Li, Xu Jia +2
In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for su…