Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-training with pseudo-labels since ground-truth cannot be obtained from test data. Although the quality of pseudo labels is important for stable and accurate long-term adaptation, it has not been previously addressed. In this work, we propose DPLOT, a simple yet effective TTA framework that consists of two components: (1) domain-specific block selection and (2) pseudo-label generation using paired-view images. Specifically, we select blocks that involve domain-specific feature extraction and train these blocks by entropy minimization. After blocks are adjusted for current test domain, we generate pseudo-labels by averaging given test images and corresponding flipped counterparts. By simply using flip augmentation, we prevent a decrease in the quality of the pseudo-labels, which can be caused by the domain gap resulting from strong augmentation. Our experimental results demonstrate that DPLOT outperforms previous TTA methods in CIFAR10-C, CIFAR100-C, and ImageNet-C benchmarks, reducing error by up to 5.4%, 9.1%, and 2.9%, respectively. Also, we provide an extensive analysis to demonstrate effectiveness of our framework. Code is available at https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-TTA.

Abstract Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a M ultisc A le R elational T ransformer ( MART ) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, which comprises a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE/FDE by 3.9%/11.8% on the NBA dataset. Code is available at https://github.com/gist-ailab/MART .

Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a critical issue in medical datasets and can significantly degrade model performance. Previous clean sample selection methods have not utilized the well pre-trained features of vision foundation models (VFMs) and assumed that training begins from scratch. In this paper, we propose CUFIT, a curriculum fine-tuning paradigm of VFMs for medical image classification under label noise. Our method is motivated by the fact that linear probing of VFMs is relatively unaffected by noisy samples, as it does not update the feature extractor of the VFM, thus robustly classifying the training samples. Subsequently, curriculum fine-tuning of two adapters is conducted, starting with clean sample selection from the linear probing phase. Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks. Specifically, our method surpasses previous baselines by 5.0\%, 2.1\%, 4.6\%, and 5.8\% at a 40\% noise rate on the HAM10000, APTOS-2019, BloodMnist, and OrgancMnist datasets, respectively. Furthermore, we provide extensive analyses to demonstrate the impact of our method on noisy label detection. For instance, our method shows higher label precision and recall compared to previous approaches. Our work highlights the potential of leveraging VFMs in medical image classification under challenging conditions of noisy labels.

Out-of-distribution (OOD) detection involves binary classification whether the given data is from outside the training data or not. Previous studies proposed outlier exposure (OE) that trains the model on an outlier dataset designed to represent potential future OOD data, thereby enhancing OOD detection performance. However, obtaining an outlier dataset representing all possible future OOD data can be challenging, and such dataset may be unavailable in some cases. This study proposes a novel approach to expose the model to jigsaw puzzles generated from training images as the outlier data. Specifically, the model is trained to have a low LogitNorm for given jigsaw puzzles. We argue that jigsaw puzzles can effectively represent future OOD data because they contain similar background information as the in-distribution data but with their semantic information destroyed. Our experimental results demonstrate that our approach outperforms previous competitive OOD detection methods and effectively detects semantically shifted OOD examples. Our code is available at https://github.com/gist-ailab/jigsaw-training-OOD.

Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult. However, learning representation from raw EEG signals is challenging owing to the following issues: (1) sleep-related EEG patterns occur on different temporal and frequency scales and 2) sleep stages share similar EEG patterns. To address these issues, we propose an automatic Sleep scoring framework that incorporates (1) a feature Pyramid and 2) supervised Contrastive learning, named SleePyCo. For the feature pyramid, we propose a backbone network named SleePyCo-backbone to consider multiple feature sequences on different temporal and frequency scales. Supervised contrastive learning allows the network to extract class discriminative features by minimizing the distance between intra-class features and simultaneously maximizing that between inter-class features. Comparative analyses on four public datasets demonstrate that SleePyCo consistently outperforms existing frameworks based on single-channel EEG. Extensive ablation experiments show that SleePyCo exhibited an enhanced overall performance, with significant improvements in discrimination between sleep stages, especially for N1 and rapid eye movement (REM). Source code is available at https://github.com/gist-ailab/SleePyCo.

Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods typically relied on the highly activated feature map outputted by the network. In this study, we revealed that the norm of the feature map obtained from a block other than the last block can serve as a better indicator for OOD detection. To leverage this insight, we propose a simple framework that comprises two metrics: FeatureNorm, which computes the norm of the feature map, and NormRatio, which calculates the ratio of FeatureNorm for ID and OOD samples to evaluate the OOD detection performance of each block. To identify the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create jigsaw puzzles as pseudo OOD from ID training samples and compute NormRatio, selecting the block with the highest value. After identifying the suitable block, OOD detection using FeatureNorm outperforms other methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and highlight the significance of block selection, which can also improve previous OOD detection methods. Our code is available at https://github.com/gistailab/block-selection-for-OOD-detection.