Coreset Selection for Object Detection

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP$_{50}$ when selecting 200 images.

HourglassNeRF: Casting an Hourglass as a Bundle of Rays for Few-shot Neural Rendering

Recent advancements in the Neural Radiance Field (NeRF) have bolstered its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge. Addressing this, we propose HourglassNeRF, an effective regularization-based approach with a novel hourglass casting strategy. Our proposed hourglass is conceptualized as a bundle of additional rays within the area between the original input ray and its corresponding reflection ray, by featurizing the conical frustum via Integrated Positional Encoding (IPE). This design expands the coverage of unseen views and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Furthermore, we propose luminance consistency regularization based on the Lambertian assumption, which is known to be effective for training a set of augmented rays under the few-shot setting. Leveraging the inherent property of a Lambertian surface, which retains consistent luminance irrespective of the viewing angle, we assume our proposed hourglass as a collection of flipped diffuse reflection rays and enhance the luminance consistency between the original input ray and its corresponding hourglass, resulting in more physically grounded training framework and performance improvement. Our HourglassNeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details. The code will be available.

Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.

Sparse MDOD: Training End-to-End Multi-Object Detector without Bipartite Matching

Recent end-to-end multi-object detectors simplify the inference pipeline by removing the hand-crafted process such as the duplicate bounding box removal using non-maximum suppression (NMS). However, in the training, they require bipartite matching to calculate the loss from the output of the detector. Contrary to the directivity of the end-to-end method, the bipartite matching makes the training of the end-to-end detector complex, heuristic, and reliant. In this paper, we aim to propose a method to train the end-to-end multi-object detector without bipartite matching. To this end, we approach end-to-end multi-object detection as a density estimation using a mixture model. Our proposed detector, called Sparse Mixture Density Object Detector (Sparse MDOD) estimates the distribution of bounding boxes using a mixture model. Sparse MDOD is trained by minimizing the negative log-likelihood and our proposed regularization term, maximum component maximization (MCM) loss that prevents duplicated predictions. During training, no additional procedure such as bipartite matching is needed, and the loss is directly computed from the network outputs. Moreover, our Sparse MDOD outperforms the existing detectors on MS-COCO, a renowned multi-object detection benchmark.

Few-Shot Object Detection by Attending to Per-Sample-Prototype

Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining enough annotated images for supervised object detection, it results in a far inferior performance compared to the conventional object detection methods. In this paper, we propose a meta-learning-based approach that considers the unique characteristics of each support sample. Rather than simply averaging the information of the support samples to generate a single prototype per category, our method can better utilize the information of each support sample by treating each support sample as an individual prototype. Specifically, we introduce two types of attention mechanisms for aggregating the query and support feature maps. The first is to refine the information of few-shot samples by extracting shared information between the support samples through attention. Second, each support sample is used as a class code to leverage the information by comparing similarities between each support feature and query features. Our proposed method is complementary to the previous methods, making it easy to plug and play for further improvement. We have evaluated our method on PASCAL VOC and COCO benchmarks, and the results verify the effectiveness of our method. In particular, the advantages of our method are maximized when there is more diversity among support data.

Density Estimation and Incremental Learning of Latent Vector for Generative Autoencoders

In this paper, we treat the image generation task using the autoencoder, a representative latent model. Unlike many studies regularizing the latent variable's distribution by assuming a manually specified prior, we approach the image generation task using an autoencoder by directly estimating the latent distribution. To do this, we introduce 'latent density estimator' which captures latent distribution explicitly and propose its structure. In addition, we propose an incremental learning strategy of latent variables so that the autoencoder learns important features of data by using the structural characteristics of under-complete autoencoder without an explicit regularization term in the objective function. Through experiments, we show the effectiveness of the proposed latent density estimator and the incremental learning strategy of latent variables. We also show that our generative model generates images with improved visual quality compared to previous generative models based on autoencoders.