353 citations · 701 across the 20 of their papers we have counts for
9 papers · 1 filter
ViTPose++: Vision Transformer for Generic Body Pose Estimation
Yufei Xu, Jing Zhang, Qiming Zhang +1
In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalabilit…
Rethinking Hierarchies in Pre-trained Plain Vision Transformer
Yufei Xu, Jing Zhang, Qiming Zhang +1
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. However, customized algorithms should be carefully designed fo…
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Di Wang, Qiming Zhang, Yufei Xu +4
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalabili…
Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detection
Zhe Chen, Jing Zhang, Yufei Xu +1
Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) which aims to mitigate the gap between features from different levels and…
CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose
Xu Zhang, Wen Wang, Zhe Chen +3
Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of v…
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
Yuxiang Yang, Junjie Yang, Yufei Xu +3
Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus…
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
Yufei Xu, Jing Zhang, Qiming Zhang +1
Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort ha…
VSA: Learning Varied-Size Window Attention in Vision Transformers
Qiming Zhang, Yufei Xu, Jing Zhang +1
Attention within windows has been widely explored in vision transformers to balance the performance, computation complexity, and memory footprint. However, current models adopt a h…
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond
Qiming Zhang, Yufei Xu, Jing Zhang +1
Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism.…