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Found 6 papers, 2 papers with code
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the immediate recognition and localization of objects and events.
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis
We focus on two novel aspects of zero-shot visual reasoning: i) evaluating the impact of conveying scene information as either visual embeddings or purely textual scene descriptions to the underlying large language model (LLM) of the VLM, and ii) comparing the effectiveness of chain-of-thought prompting to standard prompting for zero-shot visual reasoning.
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
Consistent with the findings of QUAG, we find that most of the models achieve near-trivial performance on CLAVI.
A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods
Our resulting knowledge representation framework can encode a wider variety of world knowledge and represent beliefs flexibly using grounded concepts as well as free-text phrases.
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks.
graph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
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