activity
20142021
most citedDeep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

652 citations · 2.2k across the 16 of their papers we have counts for

collaborators

16 papers

cs.CV2021★ 5 cited

TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking

Peng Chu, Jiang Wang, Quanzeng You +2

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerf…

cs.CV2019★ 25 cited

RC-DARTS: Resource Constrained Differentiable Architecture Search

Xiaojie Jin, Jiang Wang, Joshua Slocum +4

Recent advances show that Neural Architectural Search (NAS) method is able to find state-of-the-art image classification deep architectures. In this paper, we consider the one-shot…

cs.CV2019★ 48 cited

Adversarial Examples Improve Image Recognition

Cihang Xie, Mingxing Tan, Boqing Gong +3

Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if h…

cs.CV2018★ 7 cited

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

JIyang Gao, Jiang Wang, Shengyang Dai +2

The labeling cost of large number of bounding boxes is one of the main challenges for training modern object detectors. To reduce the dependence on expensive bounding box annotatio…

cs.LG2018★ 8 cited

Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN

Xu Chen, Jiang Wang, Hao Ge

We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discrimina…

cs.CV2016★ 16 cited

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

Xiao Liu, Jiang Wang, Shilei Wen +2

A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localiz…