Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
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8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
Agentic AI systems are required to overcome the parameter coverage ceiling that prevents foundation models from handling certain out-of-distribution cases.
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.
Merging CK+, FER+, and KDEF datasets with online/offline augmentation and random weighted sampling enables a deep CNN to classify seven facial emotions at 82% accuracy.
citing papers explorer
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Adaptive Camera Sensor for Vision Models
Lens adapts camera sensors in real time via the VisiT confidence-based quality indicator to improve vision model accuracy on domain-shifted images, shown on ImageNet-ES and a new diverse benchmark.
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A Composite Activation Function for Learning Stable Binary Representations
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
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TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.
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Medical Model Synthesis Architectures: A Case Study
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
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Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models
Agentic AI systems are required to overcome the parameter coverage ceiling that prevents foundation models from handling certain out-of-distribution cases.
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WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval
WRF4CIR uses weight-regularized fine-tuning with adversarial perturbations to mitigate overfitting in composed image retrieval and narrows the generalization gap on benchmarks.
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A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.
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Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies
Merging CK+, FER+, and KDEF datasets with online/offline augmentation and random weighted sampling enables a deep CNN to classify seven facial emotions at 82% accuracy.