22 accepted papers
Domain generalization in semantic segmentation faces challenges from domain shifts, particularly under adverse conditions. While diffusion-based data generation methods show promise, they introduce inherent misalignment between generated images and semantic masks. This paper presents FLEX-Seg (FLexi
Sound source localization task aims to identify the locations of sound-emitting objects by leveraging correlations between audio and visual modalities. Most existing SSL methods rely on contrastive learning-based feature matching, but lack explicit reasoning and verification, limiting their effectiv
Pre-trained weights have become a cornerstone of modern deep learning, enabling efficient knowledge transfer and improving downstream task performance, especially in data-scarce scenarios. However, a fundamental question remains: how can we obtain better pre-trained weights that encapsulate more kno
Change captioning aims to describe changes between a pair of images. However, existing works rely on visual features alone, which often fail to capture subtle but meaningful changes because they lack the ability to represent explicitly structured information such as object relationships and composit
Monocular 3D Object Detection has achieved impressive performance on densely annotated datasets. However, it struggles when only a fraction of objects are labeled due to the high cost of 3D annotation. This sparsely-annotated setting is common in real-world scenarios where annotating every object is
Video moment retrieval (MR) and highlight detection (HD) with natural language queries aim to localize relevant moments and key highlights in a video clips. However, existing methods overlook the importance of individual words, treating the entire text query and video clips as a black-box, which hin
Multi-task learning (MTL) is critical in real-world applications such as autonomous driving and robotics, enabling simultaneous handling of diverse tasks. However, obtaining fully annotated data for all tasks is impractical due to labeling costs. Existing methods for partially labeled MTL typically
Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality…
Audio-visual sound source localization task aims to spatially localize sound-making objects within visual scenes by integrating visual and audio cues. However, existing methods struggle with accurately localizing sound-making objects in complex scenes, particularly when visually similar silent objec…
The goal of video moment retrieval and highlight detection is to identify specific segments and highlights based on a given text query. With the rapid growth of video content and the overlap between these tasks, recent works have addressed both simultaneously. However, they still struggle to fully c…
We present a novel method for Audio-Visual Question Answering (AVQA) in real-world scenarios where one modality (audio or visual) can be missing. Inspired by human cognitive processes, we introduce a Trans-Modal Associative (TMA) memory that recalls missing modal information (i.e., pseudo modal feat…
The goal of the multi-sound source localization task is to localize sound sources from the mixture individually. While recent multi-sound source localization methods have shown improved performance they face challenges due to their reliance on prior information about the number of objects to be sepa…
Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and tasks is constantly changing in a statistical way, not a static way. Although r…
Although multispectral pedestrian detection studies have shown remarkable detection performances, they are still vulnerable to adversarial attacks. We see the similarity relations between object candidates were not maintained because of the adversarial attacks, resulting in performance degradation.…
Although audio modality has the potential to solve various visually challenging conditions of visual modality, there are few studies on audio-based detection. This is because the audio modality itself contains less accurate spatial information. To alleviate this issue, the existing audio-based metho…
Recently, pedestrian detection on thermal infrared images has shown the robust pedestrian detection performance. In this paper, we propose a novel thermal infrared pedestrian detection framework which can associate and utilize the complementary pedestrian knowledge from visible images. Motivated by…
Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot cope with other modal in…
Although the visual appearances of small-scale objects are not well observed, humans can recognize them by associating the visual cues of small objects from their memorized appearance. It is called cued recall. In this paper, motivated by the memory process of humans, we introduce a novel pedestrian…
The success of multi-sensor data fusions in deep learning appears to be attributed to the use of complementary information among multiple sensor datasets. Compared to their predictive performance, relatively less attention has been devoted to the adversarial robustness of multi-sensor data fusion mo…
Recently, cybersickness assessment for VR content is required to deal with viewing safety issues. Assessing physical symptoms of individual viewers is challenging but important to provide detailed and personalized guides for viewing safety. In this paper, we propose a novel symptom-aware cybersickne…
In this paper, we propose a novel image segmentation method to tackle two critical problems of medical image, which are (i) ambiguity of structure boundary in the medical image domain and (ii) uncertainty of the segmented region without specialized domain knowledge. To solve those two problems in au…
Object detection performs two tasks (classification and localization) simultaneously. Two tasks share a similarity: they need robust features that effectively represent the visual appearance of the objects. However, two tasks also have different properties. First, classification mainly requires feat…