Published in CAAI International Conference on Artificial Intelligence (CICAI), 2022
This paper is about Adversarial and Implicit Modality Imputation with multi-modal representation learning via auto-encoding, clustering based on CPM-Net, adversarial networks and a feedback loop to resolve the modality-missing issue with application to UK Biobank database.
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Published in NeurIPS GLFrontiers Workshop, 2022
This paper is about mitigating over-smoothing and over-squashing issues in deep GNNs by proposing a normalization technique in message-passing algorithms (PowerEmbed) to encode global spectra information inspired by spectral embeddings.
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Published in NeurIPS, 2024
This paper introduces Selective Projection Decay (SPD), a weight decay technique that selectively regularizes certain layers to balance fitting and retaining pre-trained knowledge, improving generalization and robustness when fine-tuning foundation models.
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Published in ICLR, 2025
This paper introduces Directional Gradient Projection (DiGraP), a novel layer-wise trainable method that incorporates directional information from gradients to bridge regularization and multi-objective optimization. Besides demonstrating our method on image classification, as another contribution we generalize this area to the multi-modal evaluation settings for robust fine-tuning.
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Published in CVPR, 2025
We introduce FRAMES-VQA, a benchmark designed to evaluate robust fine-tuning strategies for visual question answering (VQA) under diverse multi-modal distribution shifts. By leveraging ten existing VQA datasets categorized into in-distribution (ID), near-OOD, and far-OOD scenarios, we systematically analyze the impact of uni-modal, multi-modal, and adversarial shifts. Our study compares existing robust fine-tuning methods, quantifies distribution shifts using Mahalanobis distance, and explores the interactions between uni- and multi-modal shifts, providing valuable insights for developing more robust VQA models.
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