3.5k citations · 3.9k across the 17 of their papers we have counts for
18 papers
Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention
Yifan Liu, Youbao Tang, Ning Zhang +2
Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video. Current approaches treat video frames homog…
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
Suvir Mirchandani, Licheng Yu, Mengjiao Wang +4
Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item gi…
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment
Mingyang Zhou, Licheng Yu, Amanpreet Singh +3
Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require p…
Connecting What to Say With Where to Look by Modeling Human Attention Traces
Zihang Meng, Licheng Yu, Ning Zhang +4
We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos
Xuecheng Nie, Yuncheng Li, Linjie Luo +2
Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and…
Weakly Supervised Body Part Segmentation with Pose based Part Priors
Zhengyuan Yang, Yuncheng Li, Linjie Yang +2
Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good perfor…
Context-Aware Zero-Shot Recognition
Ruotian Luo, Ning Zhang, Bohyung Han +1
We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context. Contrary to the traditional zero-shot learning methods, which s…
Laplace Landmark Localization
Joseph P Robinson, Yuncheng Li, Ning Zhang +2
Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped inte…
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints
Zhe Li, Xuehan Xiong, Zhou Ren +3
Recently, there emerged revived interests of designing automatic programs (e.g., using genetic/evolutionary algorithms) to optimize the structure of Convolutional Neural Networks (…
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Zhou Ren, Xiaoyu Wang, Ning Zhang +2
Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep…
AutoScaler: Scale-Attention Networks for Visual Correspondence
Shenlong Wang, Linjie Luo, Ning Zhang +1
Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discrimi…
Fine-grained pose prediction, normalization, and recognition
Ning Zhang, Evan Shelhamer, Yang Gao +1
Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approach…
Compact Bilinear Pooling
Yang Gao, Oscar Beijbom, Ning Zhang +1
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. Howe…
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Ning Zhang, Manohar Paluri, Yaniv Taigman +2
We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset,…
Do Convnets Learn Correspondence?
Jonathan Long, Ning Zhang, Trevor Darrell
Convolutional neural nets (convnets) trained from massive labeled datasets have substantially improved the state-of-the-art in image classification and object detection. However, v…
Part-based R-CNNs for Fine-grained Category Detection
Ning Zhang, Jeff Donahue, Ross Girshick +1
Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-…
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato +2
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, a…
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals +4
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks c…