active_learning

This paper presents Neural Visibility Field (NVF) a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region resulting in increased uncertainty in the synthesized views. To address this we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently NVF naturally assigns higher uncertainty to unobserved regions aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping outperforming existing methods. More details can be found at https://sites.google.com/view/nvf-cvpr24/.

We present NARUTO a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning enabling high-fidelity surface reconstruction. Our approach leverages a multi-resolution hash-grid as the mapping backbone chosen for its exceptional convergence speed and capacity to capture high-frequency local features. The centerpiece of our work is the incorporation of an uncertainty learning module that dynamically quantifies reconstruction uncertainty while actively reconstructing the environment. By harnessing learned uncertainty we propose a novel uncertainty aggregation strategy for goal searching and efficient path planning. Our system autonomously explores by targeting uncertain observations and reconstructs environments with remarkable completeness and fidelity. We also demonstrate the utility of this uncertainty-aware approach by enhancing SOTA neural SLAM systems through an active ray sampling strategy. Extensive evaluations of NARUTO in various environments using an indoor scene simulator confirm its superior performance and state-of-the-art status in active reconstruction as evidenced by its impressive results on benchmark datasets like Replica and MP3D.

The pretraining-finetuning paradigm has gained popularity in various computer vision tasks. In this paradigm the emergence of active finetuning arises due to the abundance of large-scale data and costly annotation requirements. Active finetuning involves selecting a subset of data from an unlabeled pool for annotation facilitating subsequent finetuning. However the use of a limited number of training samples can lead to a biased distribution potentially resulting in model overfitting. In this paper we propose a new method called ActiveDC for the active finetuning tasks. Firstly we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space. Secondly we calibrate the distribution of the selected samples by exploiting implicit category information in the unlabeled pool. The feature visualization provides an intuitive sense of the effectiveness of our approach to distribution calibration. We conducted extensive experiments on three image classification datasets with different sampling ratios. The results indicate that ActiveDC consistently outperforms the baseline performance in all image classification tasks. The improvement is particularly significant when the sampling ratio is low with performance gains of up to 10%. Our code will be released.

Pre-trained Vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks such as classification and retrieval. Despite their performance because improving performance on new tasks requires task-specific knowledge their adaptation is essential. While labels are needed for the adaptation acquiring them is typically expensive. To overcome this challenge active learning a method of achieving a high performance by obtaining labels for a small number of samples from experts has been studied. Active learning primarily focuses on selecting unlabeled samples for labeling and leveraging them to train models. In this study we pose the question "how can the pre-trained VLMs be adapted under the active learning framework?" In response to this inquiry we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling. Based on these observations we devise a novel active learning framework for VLMs denoted as PCB. To assess the effectiveness of our approach we conduct experiments on seven different real-world datasets and the results demonstrate that PCB surpasses conventional active learning and random sampling methods.

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task which endeavors to cluster unlabeled samples from both novel and old classes leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes and that novel categories are fully unlabeled GCD inherently faces intractable problems including imbalanced classification performance and inconsistent confidence between old and new classes especially in the low-labeling regime. Hence some annotations of new classes are deemed necessary. However labeling new classes is extremely costly. To address this issue we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem we devise an adaptive sampling strategy which jointly considers novelty informativeness and diversity to adaptively select novel samples with proper uncertainty. However owing to the varied orderings of label indices caused by the clustering of novel classes the queried labels are not directly applicable to subsequent training. To overcome this issue we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD