162 citations · 896 across the 37 of their papers we have counts for
11 papers · 1 filter
Generative One-Shot Face Recognition
Zhengming Ding, Yandong Guo, Lei Zhang +1
One-shot face recognition measures the ability to identify persons with only seeing them at one glance, and is a hallmark of human visual intelligence. It is challenging for conven…
Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Minghan Li, Xiangyong Cao, Qian Zhao +3
Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of…
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…
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning
Ming Jiang, Junjie Hu, Qiuyuan Huang +3
Popular metrics used for evaluating image captioning systems, such as BLEU and CIDEr, provide a single score to gauge the system's overall effectiveness. This score is often not in…
TIGEr: Text-to-Image Grounding for Image Caption Evaluation
Ming Jiang, Qiuyuan Huang, Lei Zhang +5
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching b…
Unified Vision-Language Pre-Training for Image Captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang +3
This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image cap…
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Bowen Cheng, Bin Xiao, Jingdong Wang +3
Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRN…
Learning to Count Objects with Few Exemplar Annotations
Jianfeng Wang, Rong Xiao, Yandong Guo +1
In this paper, we study the problem of object counting with incomplete annotations. Based on the observation that in many object counting problems the target objects are normally r…
High Frequency Residual Learning for Multi-Scale Image Classification
Bowen Cheng, Rong Xiao, Jianfeng Wang +2
We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. T…
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
Lei Zhang, Zhiqiang Lang, Peng Wang +4
Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction for SSR is to learn a complicated mapping fun…
Object-driven Text-to-Image Synthesis via Adversarial Training
Wenbo Li, Pengchuan Zhang, Lei Zhang +4
In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes. Following the tw…