output
20142026
most citedNTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

1.8k citations

Showing 2020 · cs.CVShow all

19 papers · 2 filters

cs.CV20209 cited

Heuristic Domain Adaptation

Shuhao Cui, Xuan Jin, Shuhui Wang +2

In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different…

cs.CV202041 cited

Distortion-aware Monocular Depth Estimation for Omnidirectional Images

Hong-Xiang Chen, Kunhong Li, Zhiheng Fu +3

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth esti…

cs.CV20202 cited

Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning

Huan Fu, Shunming Li, Rongfei Jia +3

Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shap…

cs.CV202064 cited

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

Shengyu Zhang, Tan Jiang, Tan Wang +6

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on…

cs.CV2020

One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction

Mengli Cheng, Minghui Qiu, Xing Shi +2

Structured information extraction from document images usually consists of three steps: text detection, text recognition, and text field labeling. While text detection and text rec…

cs.CV20205 cited

Weakly Supervised Learning with Side Information for Noisy Labeled Images

Lele Cheng, Xiangzeng Zhou, Liming Zhao +5

In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data. To tackle this problem, some image related side inf…