619 citations · 6.4k across the 178 of their papers we have counts for
24 papers · 1 filter
Similarity R-C3D for Few-shot Temporal Activity Detection
Huijuan Xu, Bingyi Kang, Ximeng Sun +3
Many activities of interest are rare events, with only a few labeled examples available. Therefore models for temporal activity detection which are able to learn from a few example…
Few-shot Object Detection via Feature Reweighting
Bingyi Kang, Zhuang Liu, Xin Wang +3
Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop…
Graph-Based Global Reasoning Networks
Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan +3
Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) exce…
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
SPIDER (Stochastic Path Integrated Differential EstimatoR) is an efficient gradient estimation technique developed for non-convex stochastic optimization. Although having been show…
-Nets: Double Attention Networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li +2
Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly i…
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Jian Zhao, Yu Cheng, Yi Cheng +12
Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face chang…
Egocentric Spatial Memory
Mengmi Zhang, Keng Teck Ma, Shih-Cheng Yen +3
Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspecti…
Finding any Waldo: zero-shot invariant and efficient visual search
Mengmi Zhang, Jiashi Feng, Keng Teck Ma +3
Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distr…
Object Relation Detection Based on One-shot Learning
Li Zhou, Jian Zhao, Jianshu Li +2
Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between…
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
Yunchao Wei, Zhiqiang Shen, Bowen Cheng +4
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C)…
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Mengmi Zhang, Gabriel Kreiman
Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We…
Multi-Fiber Networks for Video Recognition
Yunpeng Chen, Yannis Kalantidis, Jianshu Li +2
In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art a…
Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
Canyi Lu, Jiashi Feng, Zhouchen Lin +1
The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] is an interesting convex penalty induced by the tensor SVD [Kilmer and Martin, 2011]. It plays a similar role…
Understanding Generalization and Optimization Performance of Deep CNNs
Pan Zhou, Jiashi Feng
This work aims to provide understandings on the remarkable success of deep convolutional neural networks (CNNs) by theoretically analyzing their generalization performance and esta…
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
Yunchao Wei, Huaxin Xiao, Honghui Shi +3
Despite the remarkable progress, weakly supervised segmentation approaches are still inferior to their fully supervised counterparts. We obverse the performance gap mainly comes fr…
Subspace Clustering by Block Diagonal Representation
Canyi Lu, Jiashi Feng, Zhouchen Lin +2
This paper studies the subspace clustering problem. Given some data points approximately drawn from a union of subspaces, the goal is to group these data points into their underlyi…
Learning Markov Clustering Networks for Scene Text Detection
Zichuan Liu, Guosheng Lin, Sheng Yang +3
A novel framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. MCN predicts instance-level bounding boxes by firstly converting an im…
Learning Pixel-wise Labeling from the Internet without Human Interaction
Yun Liu, Yujun Shi, JiaWang Bian +3
Deep learning stands at the forefront in many computer vision tasks. However, deep neural networks are usually data-hungry and require a huge amount of well-annotated training samp…
Zigzag Learning for Weakly Supervised Object Detection
Xiaopeng Zhang, Jiashi Feng, Hongkai Xiong +1
This paper addresses weakly supervised object detection with only image-level supervision at training stage. Previous approaches train detection models with entire images all at on…
Adversarial Complementary Learning for Weakly Supervised Object Localization
Xiaolin Zhang, Yunchao Wei, Jiashi Feng +2
In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically pr…
Left-Right Comparative Recurrent Model for Stereo Matching
Zequn Jie, Pengfei Wang, Yonggen Ling +4
Leveraging the disparity information from both left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disp…
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu, Jiashi Feng, Yudong Chen +3
In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our mode…
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Jian Zhao, Jianshu Li, Yu Cheng +4
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding huma…
Cross-domain Human Parsing via Adversarial Feature and Label Adaptation
Si Liu, Yao Sun, Defa Zhu +4
Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models focus on parsing the high-resolution…