Computer vision publications
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Model-Free Generative Replay For Lifelong Reinforcement Learning: Application To Starcraft-2
We evaluate our proposed algorithms on three different scenarios comprising tasks from the Starcraft 2 and Minigrid domains.
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Head-Worn Markerless Augmented Reality Inside a Moving Vehicle
This paper describes a system that provides general head-worn outdoor AR capability for the user inside a moving vehicle.
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SIGNAV: Semantically-Informed GPS-Denied Navigation and Mapping in Visually-Degraded Environments
We present SIGNAV, a real-time semantic SLAM system to operate in perceptually-challenging situations.
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Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users' understanding of those cases.
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Challenges in Procedural Multimodal Machine Comprehension: A Novel Way to Benchmark
We identify three critical biases stemming from the question-answer generation process and memorization capabilities of large deep models.
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Global Heading Estimation for Wide Area Augmented Reality Using Road Semantics for Geo-referencing
We present a method to estimate global camera heading by associating directional information from road segments in the camera view with annotated satellite imagery.
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Long-Range Augmented Reality with Dynamic Occlusion Rendering
This paper addresses the problem of fast and accurate dynamic occlusion reasoning by real objects in the scene for large scale outdoor AR applications.
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“How to best say it?” : Translating Directives in Machine Language into Natural Language in the Blocks World
We propose a method to generate optimal natural language for block placement directives generated by a machine's planner during human-agent interactions in the blocks world.
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Comprehension Based Question Answering Using Bloom’s Taxonomy
Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions.
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MaAST: Map Attention with Semantic Transformers for Efficient Visual Navigation
Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget.
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Towards Explainable Student Group Collaboration Assessment Models Using Temporal Representations of Individual Student Role and Behavioral Cues
In this paper we propose using simple temporal-CNN deep-learning models to assess student group collaboration that take in temporal representations of individual student roles as input.
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Hyper-Dimensional Analytics of Video Action at the Tactical Edge
We review HyDRATE, a low-SWaP reconfigurable neural network architecture developed under the DARPA AIE HyDDENN (Hyper-Dimensional Data Enabled Neural Network) program.