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Found 5 papers, 3 papers with code
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata
The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation.
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions.
Ranked #1 on
Retrieval
on InfoSeek
(using extra training data)
Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning
Finally, we demonstrate a retrieval-based hateful memes detection system, which is capable of making hatefulness classification based on data unseen in training from a database.
Ranked #2 on
Meme Classification
on Hateful Memes
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance.
Ranked #1 on
Retrieval
on OK-VQA
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
For neural architecture search space design, instead of conducting neural architecture search on the whole feasible neural architecture search space, we first systematically explore the weight drifting tolerance of different neural network components, such as dropout, normalization, number of layers, and activation functions in which dropout is found to be able to improve the neural network robustness to weight drifting.
