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Found 10 papers, 3 papers with code
SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task
This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition.
Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present.
Decoding Language Spatial Relations to 2D Spatial Arrangements
We address the problem of multimodal spatial understanding by decoding a set of language-expressed spatial relations to a set of 2D spatial arrangements in a multi-object and multi-relationship setting.
Giving Commands to a Self-driving Car: A Multimodal Reasoner for Visual Grounding
We propose a new spatial memory module and a spatial reasoner for the Visual Grounding (VG) task.
Ranked #10 on
Referring Expression Comprehension
on Talk2Car
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?
Feed-forward networks are widely used in cross-modal applications to bridge modalities by mapping distributed vectors of one modality to the other, or to a shared space.
Learning Representations Specialized in Spatial Knowledge: Leveraging Language and Vision
Here, we move one step forward in this direction and learn such representations by leveraging a task consisting in predicting continuous 2D spatial arrangements of objects given object-relationship-object instances (e. g., {``}cat under chair{''}) and a simple neural network model that learns the task from annotated images.
Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e. g., "glass on table"), here we extend this concept to implicit spatial language, i. e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e. g., "man riding horse").
Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings
Integrating visual and linguistic information into a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision.
Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations
Human concept representations are often grounded with visual information, yet some aspects of meaning cannot be visually represented or are better described with language.
Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data
An alternative is to use a so-called threshold-moving method that a posteriori changes the decision threshold of a model to counteract the imbalance, thus has a potential to adapt to the performance measure of interest.
