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Found 6 papers, 1 papers with code
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection
With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans.
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?
We train two Language Models in a multitask fashion whereby the second model differs from the first only in that it has two additional datasets added to the training regime that are designed to impart simple numerical reasoning strategies of a sort known to improve performance on some of our evaluation datasets but not on others.
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval
When provided with sufficient explanatory context, smaller Language Models have been shown to exhibit strong reasoning ability on challenging short-answer question-answering tasks where the questions are unseen in training.
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
We equip a smaller Language Model to generalise to answering challenging compositional questions that have not been seen in training.
Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes
However, they are unable to exploit spatial information of local visual features and lack a structure that unifies semantic objects and visual features, therefore limiting the symbiosis between the two components.
SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words
Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points.
Loop Closure Detection
Simultaneous Localization and Mapping
