Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Counterfactual attention learning for fine-grained visual categorization and re-identification
Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …
tasks. In this paper, we present a counterfactual attention learning method to learn more …
Causal intervention for weakly-supervised semantic segmentation
We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Unbiased scene graph generation from biased training
Today's scene graph generation (SGG) task is still far from practical, mainly due to the
severe training bias, eg, collapsing diverse" human walk on/sit on/lay on beach" into" human …
severe training bias, eg, collapsing diverse" human walk on/sit on/lay on beach" into" human …
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …
users, which is opposed to suggesting popular items. However, the normal training …
A survey on causal inference
Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …
computer science, education, public policy, and economics, for decades. Nowadays …
Deciphering spatio-temporal graph forecasting: A causal lens and treatment
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
Counterfactual vqa: A cause-effect look at language bias
Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …
Cross-modal causal relational reasoning for event-level visual question answering
Existing visual question answering methods often suffer from cross-modal spurious
correlations and oversimplified event-level reasoning processes that fail to capture event …
correlations and oversimplified event-level reasoning processes that fail to capture event …