406 citations · 1.8k across the 130 of their papers we have counts for
10 papers · 1 filter
Recognizing Instagram Filtered Images with Feature De-stylization
Zhe Wu, Zuxuan Wu, Bharat Singh +1
Deep neural networks have been shown to suffer from poor generalization when small perturbations are added (like Gaussian noise), yet little work has been done to evaluate their ro…
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang +1
This paper presents LiteEval, a simple yet effective coarse-to-fine framework for resource efficient video recognition, suitable for both online and offline scenarios. Exploiting d…
Learning from Noisy Anchors for One-stage Object Detection
Hengduo Li, Zuxuan Wu, Chen Zhu +3
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their inte…
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
Zuxuan Wu, Ser-Nam Lim, Larry Davis +1
We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectn…
ACE: Adapting to Changing Environments for Semantic Segmentation
Zuxuan Wu, Xin Wang, Joseph E. Gonzalez +2
Deep neural networks exhibit exceptional accuracy when they are trained and tested on the same data distributions. However, neural classifiers are often extremely brittle when conf…
An Analysis of Pre-Training on Object Detection
Hengduo Li, Bharat Singh, Mahyar Najibi +2
We provide a detailed analysis of convolutional neural networks which are pre-trained on the task of object detection. To this end, we train detectors on large datasets like OpenIm…
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning
Peng Zhou, Long Mai, Jianming Zhang +3
Incremental learning targets at achieving good performance on new categories without forgetting old ones. Knowledge distillation has been shown critical in preserving the performan…
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib +2
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Visi…
Compatible and Diverse Fashion Image Inpainting
Xintong Han, Zuxuan Wu, Weilin Huang +2
Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibilit…
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Chih-Yao Ma, Jiasen Lu, Zuxuan Wu +4
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the ag…