49 citations · 123 across the 37 of their papers we have counts for
7 papers · 2 filters
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen +2
Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human a…
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks
Chengxi Li, Yue Meng, Stanley H. Chan +1
To enable intelligent automated driving systems, a promising strategy is to understand how human drives and interacts with road users in complicated driving situations. In this pap…
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes
Abhishek Patil, Srikanth Malla, Haiming Gang +1
3D multi-object detection and tracking are crucial for traffic scene understanding. However, the community pays less attention to these areas due to the lack of a standardized benc…
Graph-RISE: Graph-Regularized Image Semantic Embedding
Da-Cheng Juan, Chun-Ta Lu, Zhen Li +7
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this p…
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Teruhisa Misu +2
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each…
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Xitong Yang +2
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximatio…