15 accepted papers
Multi-modal spatio-temporal forecasting underpins many real-world applications but remains challenging due to the complex and evolving interactions across modalities and time steps. Moreover, the lack of interpretability in existing models limits their reliability in safety-critical scenarios. In th…
Remote sensing (RS) image–text retrieval faces significant challenges in real-world datasets due to the presence of Pseudo-Matched Pairs (PMPs), semantically mismatched or weakly aligned image–text pairs, which hinder the learning of reliable cross-modal alignments. To address this issue, we propose
Long video understanding is challenging due to dense visual redundancy, long-range temporal dependencies, and the tendency of chain-of-thought and retrieval-based agents to accumulate semantic drift and correlation-driven errors. We argue that long-video reasoning should begin not with reactive retr
Accurate detection of dust storms is challenging due to complex meteorological interactions. With the development of deep learning, deep neural networks have been increasingly applied to dust storm detection, offering better learning and generalization capabilities compared to traditional physical m…
Tropical Cyclone (TC) estimation aims to accurately estimate various TC attributes in real time. However, distribution shifts arising from the complex and dynamic nature of TC environmental fields, such as varying geographical conditions and seasonal changes, present significant challenges to reliab…
Cross-modal retrieval aims to bridge the semantic gap between different modalities, such as visual and textual data, enabling accurate retrieval across them. Despite significant advancements with models like CLIP that align cross-modal representations, a persistent challenge remains: the hubness pro…
Meteorological disasters, especially extreme precipitation, cause significant socioeconomic damage, highlighting the need for effective quantitative precipitation nowcasting. Existing methods, often data-driven and resource-intensive, struggle to capture the underlying physical laws of meteorology.…
The complex and diverse underwater environment causes various types of degradation in underwater images. However, most existing methods focus on single underwater datasets, where the similarities in degradation limit the model’s exploration of different degradation characteristics. To address this c…
Tropical cyclones (TCs) are complex weather systems with strong winds and heavy rainfall, causing substantial loss of life and property. Therefore, accurate TC forecasting is crucial for the effective prevention of disasters caused by TCs. TC forecasting can be regarded as a spatio-temporal predicti…
Deep learning methods have made significant progress in regular rainfall forecasting, yet the more hazardous tropical cyclone (TC) rainfall has not received the same attention. While regular rainfall models can offer valuable insights for designing TC rainfall forecasting models, most existing metho…
Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks. However, the significant differences between industrial and natural scenes make applying LVLMs challenging. Existing LVLMs rely on user-provided prompts to segment objects. This often leads to suboptimal performan…
Accurate forecasting of tropical cyclone (TC) plays a critical role in the prevention and defense of TC disasters. We must explore a more accurate method for TC prediction. Deep learning methods are increasingly being implemented to make TC prediction more accurate. However, most existing methods la…
Capturing rich multi-scale features is essential for resolving complex variations in medical image segmentation. In this paper, we explore how to fully utilize the advantages of Convolutional neural networks (CNN) and Transformer, and propose a novel multi-stage aggregation architecture named MA-Tra…
HOI detection is essential for human-computer interaction, especially in behavior detection and robot manipulation. Existing mainstream transformer methods of HOI detection are focused on single-stream detection only, e.g., image \rightarrow HOI(\mathcal{P}_{1})image \rightarrow HOI(\mathcal{P}_{1})…
Most instance segmentation models are not end-to-end trainable due to either the incorporation of proposal estimation (RPN) as a pre-processing or non-maximum suppression (NMS) as a post-processing. Here we propose a novel end-to-end instance segmentation method termed ISDA. It reshapes the task int…