• Publications
  • Influence
Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification
TLDR
A supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images that outperforms state-of-the-arts on public benchmarks of similar image search and achieves promising results in the application of person re-identification in surveillance.
Cost-Effective Active Learning for Deep Image Classification
TLDR
This paper proposes a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner and incorporates deep convolutional neural networks into AL.
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
TLDR
A strong baseline is proposed, called Match R- CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner to address issues of DeepFashion2.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content
TLDR
This work proposes a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN), which can generate photo-realistic images with much better perceptual quality and richer fine-details.
Parser-Free Virtual Try-on via Distilling Appearance Flows
TLDR
This work proposes a novel approach, "teacher-tutor-student" knowledge distillation, which is able to produce highly photo-realistic images without human parsing, possessing several appealing advantages compared to prior arts.
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
TLDR
The non-trivial 3D visual grounding task has been effectively re-formulated as a simplified instance-matching problem, considering that instance-level candidates are more rational than the redundant 3D object proposals.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
TLDR
This work presents Groupable ConvNet (GroupNet) built by using a novel dynamic grouping convolution (DGConv) operation, which is able to learn the number of groups in an end-to-end manner and outperforms its counterparts such as ResNet and ResNeXt in terms of accuracy and computational complexity.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
TLDR
A novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors is proposed, which inherently improves SS optimization through fully end-to-end training.
Attentive Crowd Flow Machines
TLDR
A unified neural network module, called Attentive Crowd Flow Machine~ (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism, and achieves significant improvements over the state-of-the-art methods.
Geometric Scene Parsing with Hierarchical LSTM
TLDR
This paper proposes a novel recurrent neural network model, named H-LSTM, capable of parsing scene geometric structures and outperforming several state-of-the-art methods by large margins and shows promising 3D reconstruction results from the still images based on the geometric parsing.
...
1
2
3
4
...