activity
20182023
most citedImproving Image Captioning with Conditional Generative Adversarial Nets

77 citations · 116 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023★ 1 cited

Delving into Identify-Emphasize Paradigm for Combating Unknown Bias

Bowen Zhao, Chen Chen, Qian-Wei Wang +2

Dataset biases are notoriously detrimental to model robustness and generalization. The identify-emphasize paradigm appears to be effective in dealing with unknown biases. However,…

cs.LG2023★ 8 cited

DELTA: degradation-free fully test-time adaptation

Bowen Zhao, Chen Chen, Shu-Tao Xia

Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from th…

cs.LG2021

Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment

Bowen Zhao, Chen Chen, Qian-Wei Wang +2

Models notoriously suffer from dataset biases which are detrimental to robustness and generalization. The identify-emphasize paradigm shows a promising effect in dealing with unkno…

eess.AS2021★ 1 cited

CycleFlow: Purify Information Factors by Cycle Loss

Haoran Sun, Chen Chen, Lantian Li +1

SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, ho…

cs.LG2021★ 1 cited

Energy Aligning for Biased Models

Bowen Zhao, Chen Chen, Qi Ju +1

Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem. From the perspec…

cs.CV2019★ 28 cited

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

Chen Chen, Mengyuan Liu, Xiandong Meng +2

Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Ther…

cs.CV2018★ 77 cited

Improving Image Captioning with Conditional Generative Adversarial Nets

Chen Chen, Shuai Mu, Wanpeng Xiao +3

In this paper, we propose a novel conditional-generative-adversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-…