349 citations · 2.5k across the 74 of their papers we have counts for
8 papers · 1 filter
CLAWSAT: Towards Both Robust and Accurate Code Models
Jinghan Jia, Shashank Srikant, Tamara Mitrovska +4
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfusca…
Masked Motion Encoding for Self-Supervised Video Representation Learning
Xinyu Sun, Peihao Chen, Liangwei Chen +4
How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by pr…
Gait Recognition in the Wild with Multi-hop Temporal Switch
Jinkai Zheng, Xinchen Liu, Xiaoyan Gu +5
Existing studies for gait recognition are dominated by in-the-lab scenarios. Since people live in real-world senses, gait recognition in the wild is a more practical problem that h…
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Hongbin Lin, Yifan Zhang, Zhen Qiu +4
This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes,…
On-Device Training Under 256KB Memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen +3
On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. Users can benefit from customized AI models without having…
SNAKE: Shape-aware Neural 3D Keypoint Field
Chengliang Zhong, Peixing You, Xiaoxue Chen +6
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detecti…
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Han Cai, Junyan Li, Muyan Hu +2
High-resolution dense prediction enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost ma…
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
Xueyi Liu, Xiaomeng Xu, Anyi Rao +2
Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specif…