activity
20162023
most citedReference-based Defect Detection Network

49 citations · 189 across the 18 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.CV2019★ 3 cited

Endowing Deep 3D Models with Rotation Invariance Based on Principal Component Analysis

Zelin Xiao, Hongxin Lin, Renjie Li +2

In this paper, we propose a simple yet effective method to endow deep 3D models with rotation invariance by expressing the coordinates in an intrinsic frame determined by the objec…

cs.CV2019

WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection

Zhaoyang Zeng, Bei Liu, Jianlong Fu +2

We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations. Predominant works integrate region prop…

cs.CV2019

Deep Metric Learning with Density Adaptivity

Yehao Li, Ting Yao, Yingwei Pan +2

The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondenc…

cs.CV2019

Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables

Hongxin Lin, Zelin Xiao, Yang Tan +2

Deep models are capable of fitting complex high dimensional functions while usually yielding large computation load. There is no way to speed up the inference process by classical…

cs.CV2019★ 12 cited

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

Jingwen Chen, Yingwei Pan, Yehao Li +3

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an inpu…

cs.CV2019★ 3 cited

Pointing Novel Objects in Image Captioning

Yehao Li, Ting Yao, Yingwei Pan +2

Image captioning has received significant attention with remarkable improvements in recent advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be suffi…