8 accepted papers
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in visual reasoning across diverse tasks. These tasks place different demands on visual representations: some prioritize high-level global context, while others emphasize fine-grained local details. However, most existing
Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on more complex tasks involving mathematics and logic. However, their proficiency in tasks requiring both fine-grain
Hallucination in Large Vision-Language Models (LVLMs) remains a critical challenge, undermining their reliability in real-world applications. Existing studies have investigated the causes of hallucination at the modality level and proposed effective strategies. However, interaction patterns beyond
The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the…
Referring Visual Grounding (RVG) tasks revolve around utilizing vision-language interactions to incorporate object information from language expressions, thereby enabling targeted object detection or segmentation within images. Transformer-based methods have enabled effective interaction through att…
Single-domain generalization (SDG) can efficiently enhance model generalization while avoiding high annotation costs and privacy concerns. However, existing SDG methods are mainly based on data manipulation and meta-learning, which are not efficient enough due to the limited generalization performan…
Conventional unsupervised domain adaptation (UDA) and domain generalization (DG) methods rely on the assumption that all source domains can be directly accessed and combined for model training. However, this centralized training strategy may violate privacy policies in many real-world applications.…
Referring image segmentation aims to segment an object out of an image via a specific language expression. The main concept is establishing global visual-linguistic relationships to locate the object and identify boundaries using details of the image. Recently, various Transformer-based techniques h…