We propose Hyper-ICL, a lightweight framework for demonstration-free multimodal in-context learning that reconstructs demonstration effects through logit-level attention calibration, query-adaptive modulation, and layer-wise hyperbolic anchor distillation.

We propose PromptMAD, a cross-modal prompting framework for multi-class visual anomaly localization that leverages vision-language models to improve anomaly detection and localization across multiple classes.

We introduce a novel frequency-domain debiasing framework that uses consistency regularization to improve the generalization of deepfake detectors to unseen manipulations.

We propose DiSa, a directional saliency-aware prompt learning framework that improves generalization in vision-language models by integrating cross-interactive regularization and directional regularization strategies.

We propose a style-guided prompt learning framework to enhance generalization in Vision-Language models.

We propose a robust multi-class anomaly detection framework with a class-aware prompt integration mechanism to mitigate inter-class interference and a domain adapter to handle domain shifts.

We propose a novel multi-branch network with cross-attribute-guided fusion and self-attention distillation, improving face recognition in low-quality images using soft biometric attributes.

This study proposes a robust and efficient CNN-Transformer hybrid model, combining CNN locality with the global connectivity of vision Transformers. Additionally, we enhance robustness by learning smoother decision boundaries through feature mean and variance permutation within mini-batches.

We present a multi-branch network using attribute-aware integration to enhance face recognition through soft biometric prediction.

We propose consistency regularization to enhance the generalization of morph attack detection through morph-wise augmentations to enhance robustness against unseen morph attacks in biometric systems.

This paper proposes a multi-task neural network that generates a face quality vector, including nuisance factors, offering improved performance and detailed feedback for face image quality assessment.

This paper proposes a robust ensemble of CNNs and Transformers for morph detection that enhances generalization to morph attacks and increases robustness against adversarial threats through multi-perturbation training.

This paper proposes a robust transformer model that incorporates locality inductive bias and feature normalization, enhancing generalization and robustness in feature extraction tasks.

We propose a two-phase multi-expert classification method for human action recognition, addressing long-tailed distribution using super-class learning without extra data or manual annotation. A novel Graph-Based Class Selection (GCS) algorithm optimizes class configurations and inter-class dependencies.

We address the generalization issues in scene understanding for autonomous vehicles by employing GANs for weather modeling, and advanced augmentations, improving object detection robustness and generalization across domains, especially in adverse weather conditions and natural distortions.

This paper introduces a hybrid CNN-Transformer model for COVID-19 diagnosis using CT images, combining local and global feature extraction, and achieving superior performance with limited training data.

We propose a multi-teacher KD framework in which several expert CNNs, trained on different settings, supervise a lightweight student model. This framework enhances the robustness and performance of the student by using diverse knowledge sources.

This work aims to improve motion and observation models in visual object tracking. We propose a motion estimation network to refine target location predictions, with a Siamese network detecting the most probable candidate. Additionally, a weighting CNN adaptively assigns weights to similarity scores, accounting for target appearance changes.

This paper proposes a robust tracking method that exploits relationships between target patches in adjacent frames using a sparse appearance model.