Search Results
Found 60 papers, 39 papers with code
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
To this end, we disassemble three key components involved in computation of logit bias (i. e., logit features, logit predictor, and logit fusion) and empirically analyze the effect on performance of few-shot classification.
Task-Customized Mixture of Adapters for General Image Fusion
These adapters are shared across different tasks and constrained by mutual information regularization, ensuring compatibility with different tasks while complementarity for multi-source images.
Invariant Test-Time Adaptation for Vision-Language Model Generalization
Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired datasets.
Selective Learning: Towards Robust Calibration with Dynamic Regularization
The objective can be understood as seeking a model that fits the ground-truth labels by increasing the confidence while also maximizing the entropy of predicted probabilities by decreasing the confidence.
Exploring Diverse Representations for Open Set Recognition
We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space.
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
In this paper, we propose to dynamically learn the weights of SSL tasks for different nodes and fuse the embeddings learned from different SSL tasks to boost performance.
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories.
Bi-directional Adapter for Multi-modal Tracking
To handle this problem, we propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter, cross-prompting multiple modalities mutually.
Ranked #2 on
Rgb-T Tracking
on LasHeR
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
In this paper, we study the mistake bound of online kernel learning on a budget.
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection.
