456 citations · 1.1k across the 26 of their papers we have counts for
27 papers
AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation
Junchen Ding, Jialiang Dong, Yichen Zhu +5
The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the uncheck…
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
Zhenlin Xu, Yi Zhu, Tiffany Deng +6
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks li…
Improving Semantic Segmentation via Self-Training
Yi Zhu, Zhongyue Zhang, Chongruo Wu +6
Deep learning usually achieves the best results with complete supervision. In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required…
ResNeSt: Split-Attention Networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang +9
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies t…
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
Hu Zhang, Linchao Zhu, Yi Zhu +1
Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on i…
Motion-Aware Feature for Improved Video Anomaly Detection
Yi Zhu, Shawn Newsam
Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feat…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
Exploring Temporal Information for Improved Video Understanding
Yi Zhu
In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and s…
Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network…
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Yi Zhu, Karan Sapra, Fitsum A. Reda +4
Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training…
Random Temporal Skipping for Multirate Video Analysis
Yi Zhu, Shawn Newsam
Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for mul…
Gated Transfer Network for Transfer Learning
Yi Zhu, Jia Xue, Shawn Newsam
Deep neural networks have led to a series of breakthroughs in computer vision given sufficient annotated training datasets. For novel tasks with limited labeled data, the prevalent…
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper investigates conditional generative adversarial networks (cGANs) to overcome a fundamental limitation of using geotagged media for geographic discovery, namely its spars…
Learning Optical Flow via Dilated Networks and Occlusion Reasoning
Yi Zhu, Shawn Newsam
Despite the significant progress that has been made on estimating optical flow recently, most estimation methods, including classical and deep learning approaches, still have diffi…
Towards Universal Representation for Unseen Action Recognition
Yi Zhu, Yang Long, Yu Guan +2
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper pr…
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
Yi Zhu, Xueqing Deng, Shawn Newsam
We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not p…
Spatial Morphing Kernel Regression For Feature Interpolation
Xueqing Deng, Yi Zhu, Shawn Newsam
In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than…
DenseNet for Dense Flow
Yi Zhu, Shawn Newsam
Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due…
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
Yi Zhu, Sen Liu, Shawn Newsam
This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analy…
UC Merced Submission to the ActivityNet Challenge 2016
Yi Zhu, Shawn Newsam, Zaikun Xu
This notebook paper describes our system for the untrimmed classification task in the ActivityNet challenge 2016. We investigate multiple state-of-the-art approaches for action rec…
Hidden Two-Stream Convolutional Networks for Action Recognition
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical f…
Guided Optical Flow Learning
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior p…
Deep Local Video Feature for Action Recognition
Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann
We investigate the problem of representing an entire video using CNN features for human action recognition. Currently, limited by GPU memory, we have not been able to feed a whole…
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
Yi Zhu, Shawn Newsam
This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three h…