471 citations · 3.1k across the 59 of their papers we have counts for
60 papers
Self-Regulated Learning for Egocentric Video Activity Anticipation
Zhaobo Qi, Shuhui Wang, Chi Su +3
Future activity anticipation is a challenging problem in egocentric vision. As a standard future activity anticipation paradigm, recursive sequence prediction suffers from the accu…
Greedy Gradient Ensemble for Robust Visual Question Answering
Xinzhe Han, Shuhui Wang, Chi Su +2
Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information. As…
Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark
Xiujun Shu, Xiao Wang, Xianghao Zang +4
Person re-identification (re-ID) in the scenario with large spatial and temporal spans has not been fully explored. This is partially because that, existing benchmark datasets were…
Location-Sensitive Visual Recognition with Cross-IOU Loss
Kaiwen Duan, Lingxi Xie, Honggang Qi +3
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper…
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +2
Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem, which refers to making predictio…
Label Decoupling Framework for Salient Object Detection
Jun Wei, Shuhui Wang, Zhe Wu +3
To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxil…
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
Pingyang Dai, Peixian Chen, Qiong Wu +4
Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to address…
Dual Distribution Alignment Network for Generalizable Person Re-Identification
Peixian Chen, Pingyang Dai, Jianzhuang Liu +3
Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then dir…
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
Ning Wang, Wengang Zhou, Qi Tian +1
Visual tracking can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk o…
Rethinking Performance Estimation in Neural Architecture Search
Xiawu Zheng, Rongrong Ji, Qiang Wang +4
Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper,…
Projection & Probability-Driven Black-Box Attack
Jie Li, Rongrong Ji, Hong Liu +4
Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer f…
Learning to Select Base Classes for Few-shot Classification
Linjun Zhou, Peng Cui, Xu Jia +2
Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel clas…
Gradually Vanishing Bridge for Adversarial Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered t…
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary w…
Filter Sketch for Network Pruning
Mingbao Lin, Liujuan Cao, Shaojie Li +5
We propose a novel network pruning approach by information preserving of pre-trained network weights (filters). Network pruning with the information preserving is formulated as a m…
A Real-time Global Inference Network for One-stage Referring Expression Comprehension
Yiyi Zhou, Rongrong Ji, Gen Luo +5
Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description. Most ex…
Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization
Qiaokang Xie, Wengang Zhou, Guo-Jun Qi +2
Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training.…
Adaptive Graph Representation Learning for Video Person Re-identification
Yiming Wu, Omar El Farouk Bourahla, Xi Li +3
Recent years have witnessed the remarkable progress of applying deep learning models in video person re-identification (Re-ID). A key factor for video person Re-ID is to effectivel…
Multimodal Unified Attention Networks for Vision-and-Language Interactions
Zhou Yu, Yuhao Cui, Jun Yu +2
Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual co…
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models
Guoli Song, Shuhui Wang, Qingming Huang +1
Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-…
Deep Modular Co-Attention Networks for Visual Question Answering
Zhou Yu, Jun Yu, Yuhao Cui +2
Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designi…
Information Competing Process for Learning Diversified Representations
Jie Hu, Rongrong Ji, ShengChuan Zhang +4
Learning representations with diversified information remains as an open problem. Towards learning diversified representations, a new approach, termed Information Competing Process…
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval
Tao Yao, Xiangwei Kong, Lianshan Yan +2
Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, it still has so…
Multinomial Distribution Learning for Effective Neural Architecture Search
Xiawu Zheng, Rongrong Ji, Lang Tang +3
Architectures obtained by Neural Architecture Search (NAS) have achieved highly competitive performance in various computer vision tasks. However, the prohibitive computation deman…