2023-02-09 更新
Neural Congealing: Aligning Images to a Joint Semantic Atlas
Authors:Dolev Ofri-Amar, Michal Geyer, Yoni Kasten, Tali Dekel
We present Neural Congealing — a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas — a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). Notably, we design our losses and training paradigm to account only for the shared content under severe variations in appearance, pose, background clutter or other distracting objects. We demonstrate results on a plethora of challenging image sets including sets of mixed domains (e.g., aligning images depicting sculpture and artwork of cats), sets depicting related yet different object categories (e.g., dogs and tigers), or domains for which large-scale training data is scarce (e.g., coffee mugs). We thoroughly evaluate our method and show that our test-time optimization approach performs favorably compared to a state-of-the-art method that requires extensive training on large-scale datasets.
PDF Project page: https://neural-congealing.github.io/
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Gestalt-Guided Image Understanding for Few-Shot Learning
Authors:Kun Song, Yuchen Wu, Jiansheng Chen, Tianyu Hu, Huimin Ma
Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks. However, human can quickly learn the feature of a new category from very few samples. Nevertheless, previous work has rarely considered how to mimic human cognitive behavior and apply it to few-shot learning. This paper introduces Gestalt psychology to few-shot learning and proposes Gestalt-Guided Image Understanding, a plug-and-play method called GGIU. Referring to the principle of totality and the law of closure in Gestalt psychology, we design Totality-Guided Image Understanding and Closure-Guided Image Understanding to extract image features. After that, a feature estimation module is used to estimate the accurate features of images. Extensive experiments demonstrate that our method can improve the performance of existing models effectively and flexibly without retraining or fine-tuning. Our code is released on https://github.com/skingorz/GGIU.
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Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving
Authors:Jiawei Liu, Xingping Dong, Sanyuan Zhao, Jianbing Shen
Recent years have witnessed huge successes in 3D object detection to recognize common objects for autonomous driving (e.g., vehicles and pedestrians). However, most methods rely heavily on a large amount of well-labeled training data. This limits their capability of detecting rare fine-grained objects (e.g., police cars and ambulances), which is important for special cases, such as emergency rescue, and so on. To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose a simple and effective detection framework, including (1) an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects, and (2) a sample adaptive balance loss to alleviate the issue of long-tailed data distribution in autonomous driving scenarios. On the nuScenes dataset, we conduct sufficient experiments to demonstrate that our approach can successfully detect the rare (novel) classes that contain only a few training data, while also maintaining the detection accuracy of common objects.
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From Zero-Shot to Few-Shot Learning: A Step of Embedding-Aware Generative Models
Authors:Liangjun Feng, Jiancheng Zhao, Chunhui Zhao
Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual embedding spaces. Thanks to the predefined benchmark and protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue that it is time to take a step back and reconsider the embedding-aware generative paradigm. The purpose of this paper is three-fold. First, given the fact that the current embedding features in benchmark datasets are somehow out-of-date, we improve the performance of EAGMs for ZSL remarkably with embarrassedly simple modifications on the embedding features. This is an important contribution, since the results reveal that the embedding of EAGMs deserves more attention. Second, we compare and analyze a significant number of EAGMs in depth. Based on five benchmark datasets, we update the state-of-the-art results for ZSL and give a strong baseline for few-shot learning (FSL), including the classic unseen-class few-shot learning (UFSL) and the more challenging seen-class few-shot learning (SFSL). Finally, a comprehensive generative model repository, namely, generative any-shot learning (GASL) repository, is provided, which contains the models, features, parameters, and settings of EAGMs for ZSL and FSL. Any results in this paper can be readily reproduced with only one command line based on GASL.
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The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
Authors:Jun Cen, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen
Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. The UOSR draws little attention since it is proposed, but we find sometimes it is even more practical than OSR in the real world applications, as evaluation results of known but wrongly classified samples are also wrong like unknown samples. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the UOSR performance consistently surpasses the OSR performance by a large margin for the same method. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings.
PDF Accepted by ICLR 2023
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