43 citations · 113 across the 14 of their papers we have counts for
14 papers
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
Zihui Xue, Kristen Grauman
The egocentric and exocentric viewpoints of a human activity look dramatically different, yet invariant representations to link them are essential for many potential applications i…
Egocentric Video Task Translation @ Ego4D Challenge 2022
Zihui Xue, Yale Song, Kristen Grauman +1
This technical report describes the EgoTask Translation approach that explores relations among a set of egocentric video tasks in the Ego4D challenge. To improve the primary task o…
Label Encoding for Regression Networks
Deval Shah, Zi Yu Xue, Tor M. Aamodt
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regress…
Egocentric Video Task Translation
Zihui Xue, Yale Song, Kristen Grauman +1
Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in a…
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
Zihui Xue, Zhengqi Gao, Sucheng Ren +1
Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achi…
Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization
Zhengqi Gao, Sucheng Ren, Zihui Xue +2
Multimodal fusion emerges as an appealing technique to improve model performances on many tasks. Nevertheless, the robustness of such fusion methods is rarely involved in the prese…
Dynamic Multimodal Fusion
Zihui Xue, Radu Marculescu
Deep multimodal learning has achieved great progress in recent years. However, current fusion approaches are static in nature, i.e., they process and fuse multimodal inputs with id…
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Zihui Xue, Yuedong Yang, Mengtian Yang +1
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Neve…
Characterizing and Improving the Resilience of Accelerators in Autonomous Robots
Deval Shah, Zi Yu Xue, Karthik Pattabiraman +1
Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for…
Robust Counterfactual Explanations on Graph Neural Networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue +4
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuitio…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…
What Makes Multi-modal Learning Better than Single (Provably)
Yu Huang, Chenzhuang Du, Zihui Xue +3
The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
Multimodal Knowledge Expansion
Zihui Xue, Sucheng Ren, Zhengqi Gao +1
The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data. Since existing datasets and well-trained m…