Search Results for author:
Found 7 papers, 2 papers with code
Incremental Learning with Differentiable Architecture and Forgetting Search
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 industry expectations.
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
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.
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
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 instructions in unseen environments.
MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation
Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics.
Recall Loss for Imbalanced Image Classification and Semantic Segmentation
Many works have proposed to weigh the standard cross entropy loss function with pre-computed weights based on class statistics such as the number of samples and class margins.
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization
Furthermore, we present an extensive study demonstrating the contribution of each component of our model, showing $8$--$15\%$ and $4\%$ improvement from adding semantic information and our proposed attention module.
Multimodal Skip-gram Using Convolutional Pseudowords
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 raw data in another modality is immediately obtained.
