14 accepted papers
The exponential growth of streaming multi-modal data presents critical challenges for cross-modal retrieval: distribution shifts, modality gap, and scarce labels. Semi-supervised online cross-modal hashing has gained increasing interest due to its ability to encode complex streaming data and update
The synergistic framework of multimodal large language models (MLLMs) and vision foundation models demonstrates exceptional performance in image understanding tasks, yet encounters severe temporal inconsistency challenges in video segmentation scenarios. Existing methods predominantly rely on MLLMs
Due to limited features and dense object layouts, object detection in UAV images is challenging. Given that existing feature fusion methods have not fully explored the relationship between fine- and coarse-grained features, direct feature fusion can result in poor correlation between them, hindering…
Semi-supervised hashing has shown promising efficacy in large-scale image retrieval, which learns similarity-preserving codes from both labeled and unlabeled data. To enable the use of advanced supervised hashing techniques, pseudo labels are widely applied. However, existing methods typically suffe…
Online cross-modal hashing has gained increasing interest due to its ability to encode streaming data and update hash functions simultaneously. Existing online methods often assume either fully supervised or completely unsupervised settings. However, they overlook the prevalent and challenging scena…
Weakly-supervised semantic segmentation (WSSS) aims to achieve pixel-level classification under image-level supervision. Recent class activation map (CAM)-based methods seek to expand foreground activation while suppressing background. However, they often overlook the uncertainty of CAM, where non-s…
Current feature fusion strategies often fail to adequately account for the influence of activation intensity across different scales on small object features, which impedes the effective detection of small objects. To address this limitation, we propose the Region-Adaptive Feature Disentanglement an
Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or op…
The task of point cloud segmentation, comprising semantic, instance, and panoptic segmentation, has been mainly tackled by designing task-specific network architectures, which often lack the flexibility to generalize across tasks, thus resulting in a fragmented research landscape. In this paper, we…
Moment localization in videos using natural language refers to finding the most relevant segment from the video with given a query in natural language form. In this paper, we present a new boundary-determining strategy called explicit correlation-based convolution boundary locator (ECCL), which can…
Aesthetic quality assessment and semantic recognition are the two fundamental aspects of image perception and understanding tasks. Though these two tasks are related, most of the current research generally treats them as independent problems without any interaction. In this paper, we explore the rel…
With the development of medical imaging technology and machine learning, the computer-assisted diagnosis has attracted extensive research attention, which can provide beneficial reference to pathologists. However, the exponential growth of medical images and uninterpretability of traditional classif…
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data with deep learning. However, most of these met…
Hashing-based methods have made great advancements in cross-modal retrieval in both computational efficiency and storage. Learning a common space from different modalities is the common strategy of hashing-based methods, however, relational and structural information between samples in each modality…