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Critical Sampling for Data-Driven Modeling of Unknown Dynamical Systems
When a robot is exploring an unknown dynamical system, we often face the following important question:what is the minimum number of …
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Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation
Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the …
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Cross-inferential networks for source-free unsupervised domain adaptation
One central challenge in source-free unsupervised domain adaptation (UDA) is the lack of an effective approach to evaluate the …
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BDC-Adapter Brownian Distance Covariance for Better Vision-Language Reasoning
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have introduced a new paradigm for learning transferable …
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Unsupervised Prototype Adapter for Vision-Language Models
Recently, large-scale pre-trained vision-language models (e.g. CLIP and ALIGN) have demonstrated remarkable effectiveness in acquiring …
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Cycle optimization metric learning for few-shot classification
Metric learning methods are widely used in few-shot learning due to their simplicity and effectiveness. Most existing methods directly …
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Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and …
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Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization …
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Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to …
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Self-guided information for few-shot classification
Few-shot classification aims to identify novel categories using only a few labeled samples. Generally, the metric-based few-shot …
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Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation
We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the …
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