1.4k citations · 1.8k across the 32 of their papers we have counts for
13 papers · 1 filter
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
Chao Huang, Zhangjie Cao, Yunbo Wang +2
Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critic…
Open Domain Generalization with Domain-Augmented Meta-Learning
Yang Shu, Zhangjie Cao, Chenyu Wang +2
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated da…
Robust Learning Through Cross-Task Consistency
Amir Zamir, Alexander Sax, Teresa Yeo +6
Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
Bingbin Liu, Ehsan Adeli, Zhangjie Cao +4
Reasoning over visual data is a desirable capability for robotics and vision-based applications. Such reasoning enables forecasting of the next events or actions in videos. In rece…
Adversarial Cross-Domain Action Recognition with Co-Attention
Boxiao Pan, Zhangjie Cao, Ehsan Adeli +1
Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos. However, the problem of cross-domain action recogn…
Few-Shot Video Classification via Temporal Alignment
Kaidi Cao, Jingwei Ji, Zhangjie Cao +2
There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a n…
Learning to Transfer Examples for Partial Domain Adaptation
Zhangjie Cao, Kaichao You, Mingsheng Long +2
Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that eff…
Multi-Adversarial Domain Adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long +1
Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy be…
Deep Priority Hashing
Zhangjie Cao, Ziping Sun, Mingsheng Long +2
Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information. Subject to the distribut…
Partial Adversarial Domain Adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long +1
Domain adversarial learning aligns the feature distributions across the source and target domains in a two-player minimax game. Existing domain adversarial networks generally assum…
Transfer Adversarial Hashing for Hamming Space Retrieval
Zhangjie Cao, Mingsheng Long, Chao Huang +1
Hashing is widely applied to large-scale image retrieval due to the storage and retrieval efficiency. Existing work on deep hashing assumes that the database in the target domain i…
3D Object Classification via Spherical Projections
Zhangjie Cao, Qixing Huang, Karthik Ramani
In this paper, we introduce a new method for classifying 3D objects. Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neur…
Transitive Hashing Network for Heterogeneous Multimedia Retrieval
Zhangjie Cao, Mingsheng Long, Qiang Yang
Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency. Cross-modal hashing enables efficient retrieval from database of on…