6 citations · 14 across the 7 of their papers we have counts for
7 papers
Incremental Learning with Differentiable Architecture and Forgetting Search
James Seale Smith, Zachary Seymour, Han-Pang Chiu
As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to i…
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
Zachary Seymour, Niluthpol Chowdhury Mithun, Han-Pang Chiu +2
Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, u…
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
Muhammad Zubair Irshad, Niluthpol Chowdhury Mithun, Zachary Seymour +3
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language…
Striking the Right Balance: Recall Loss for Semantic Segmentation
Junjiao Tian, Niluthpol Mithun, Zach Seymour +2
Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation. Specifically, uneven class distributions in a training dataset often result…
MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation
Zachary Seymour, Kowshik Thopalli, Niluthpol Mithun +3
Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potentia…
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization
Zachary Seymour, Karan Sikka, Han-Pang Chiu +2
We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearan…
Multimodal Skip-gram Using Convolutional Pseudowords
Zachary Seymour, Yingming Li, Zhongfei Zhang
This work studies the representational mapping across multimodal data such that given a piece of the raw data in one modality the corresponding semantic description in terms of the…