Accurate and high-fidelity driving scene reconstruction relies on fully leveraging scene information as conditioning. However, existing approaches, which primarily use 3D bounding boxes and binary maps for foreground and background control, fall short in capturing the complexity of the scene and integrating multi-modal information. In this paper, we propose DualDiff, a dual-branch conditional diffusion model designed to enhance multi-view driving scene generation. We introduce Occupancy Ray Sampling (ORS), a semantic-rich 3D representation, alongside numerical driving scene representation, for comprehensive foreground and background control. To improve cross-modal information integration, we propose a Semantic Fusion Attention (SFA) mechanism that aligns and fuses features across modalities. Furthermore, we design a foreground-aware masked (FGM) loss to enhance the generation of tiny objects. DualDiff achieves state-of-the-art performance in FID score, as well as consistently better results in downstream BEV segmentation and 3D object detection tasks.

The rise of Multimodal Large Language Models (MLLMs) offers new opportunities for Micro-Expression (ME) analysis. This paper introduces Micro-Expression Visual Question Answering (ME-VQA), a novel task reformulating ME annotations (e.g., emotion categories, action units) into QA pairs. To address key challenges-hardware limitations, context inconsistency, and compositional reasoning gaps-we propose a Relationship-Aware Hierarchical VQA Framework. Our approach leverages mined emotion correlations (e.g., coarse-to-fine label dependencies) and employs a two-stage process: 1) Coarse-grained anchoring for broad emotion categories, and 2) Fine-grained reasoning constrained by coarse outputs and statistical rules. We further optimize efficiency via a dual-phase video sampling strategy: during training, keyframes (onset/apex/offset) and random non-expression frames are used; uniform sampling is applied at inference. Experiments demonstrate significant improvements in answer consistency and accuracy.

Deep learning has advanced medical image segmentation, especially for optic disc and cup detection. While convolutional neural networks (CNNs) struggle with long-range dependencies, Transformer-based architectures have emerged to address this limitation.However, complete replacement of CNNs with Transformers may impair local feature extraction. Additionally, reliance on expert-annotated datasets makes supervised learning costly. To overcome these limitations, we present DAK-Former, a novel self-supervised contrastive learning approach for optic disc and cup segmentation. Our approach introduces a new attention mechanism that combines K-nearest neighbors (KNN) with Transformer components, along with a deformable aggregation attention module to improve global feature representation. Additionally, our method uses multiple types of medical imaging data, including MRI, CT scans, and X-rays. Using contrastive learning, we match encoded queries with a dictionary of encoded keys, allowing the network to learn meaningful unsupervised feature representations. We evaluated DAK-Former on two publicly available fundus image datasets and compared it with state-of-the-art methods. Experimental results show that DAK-Former is highly effective and consistently outperforms existing approaches.

Visual Spatial Description (VSD) is an emerging image-to-text task which aims at generating descriptions of the spatial relationships between given objects in an image. In this paper, we apply Retrieval-Augmented Generation (RAG) technology in guiding Multimodal Large Language Models (MLLMs) for the task of VSD, complemented by an Adaptive Hallucination Corrector, and further fine-tuning them to bolster semantic understanding and overall model efficacy. We found that our approach demonstrated higher accuracy and fewer hallucination errors in both spatial relationship classification and visual language description tasks within the VSD task, achieving state-of-the-art results.