Search Results for author:
Found 18 papers, 10 papers with code
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.
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.
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.
Unified Normalization for Accelerating and Stabilizing Transformers
To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
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.
Scene-Adaptive Attention Network for Crowd Counting
First of all, we design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights.
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.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
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
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost.
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
Ranked #7 on
Table Recognition
on PubTabNet
Rethinking Pseudo-labeled Sample Mining for Semi-Supervised Object Detection
However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL.
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)
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion
Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data.
Extreme Network Compression via Filter Group Approximation
In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation.
