Search Results for author:
Found 4 papers, 1 papers with code
Can Better Text Semantics in Prompt Tuning Improve VLM Generalization?
Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative.
Matching Learned Causal Effects of Neural Networks with Domain Priors
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output.
Instance-wise Causal Feature Selection for Model Interpretation
We formulate a causal extension to the recently introduced paradigm of instance-wise feature selection to explain black-box visual classifiers.
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions
To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
