Search Results for author:
Found 9 papers, 6 papers with code
Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection
Current methods for open-vocabulary object detection (OVOD) rely on a pre-trained vision-language model (VLM) to acquire the recognition ability.
Learning Symmetry-Aware Geometry Correspondences for 6D Object Pose Estimation
Taking the symmetry properties of objects into consideration, we design a symmetry-aware matching loss to facilitate the learning of dense point-wise geometry features and improve the performance considerably.
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization
To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise.
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning.
UWC: Unit-wise Calibration Towards Rapid Network Compression
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance.
End-to-End Multi-Person Pose Estimation With Transformers
Current methods of multi-person pose estimation typically treat the localization and association of body joints separately.
SOIT: Segmenting Objects with Instance-Aware Transformers
The pixel-wise mask, especially, is embedded by a group of parameters to construct a lightweight instance-aware transformer.
InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation
Multi-person pose estimation is an attractive and challenging task.
Ranked #4 on
Multi-Person Pose Estimation
on COCO minival
PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images
Fast and precise object detection for high-resolution aerial images has been a challenging task over the years.
Ranked #35 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
