Search Results for author:
Found 12 papers, 10 papers with code
ChatterBox: Multi-round Multimodal Referring and Grounding
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dialogues.
VMamba: Visual State Space Model
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have long been the predominant backbone networks for visual representation learning.
Spatial Transform Decoupling for Oriented Object Detection
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks.
Ranked #1 on
Object Detection In Aerial Images
on HRSC2016
(using extra training data)
Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration
iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT).
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
A key idea of efficient implementation is to discard the masked image patches (or tokens) throughout the target network (encoder), which requires the encoder to be a plain vision transformer (e. g., ViT), albeit hierarchical vision transformers (e. g., Swin Transformer) have potentially better properties in formulating vision inputs.
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
GraFormer: Graph-Oriented Transformer for 3D Pose Estimation
In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D joints, but it is not yet well modeled.
Exploring Complicated Search Spaces with Interleaving-Free Sampling
In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on
Semantic Segmentation
on Cityscapes test
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation.
Adaptive Linear Span Network for Object Skeleton Detection
Conventional networks for object skeleton detection are usually hand-crafted.
Discretization-Aware Architecture Search
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.
