Multi-modal recommender systems (MMRS) have gained significant attention due to their ability to leverage information from various modalities to enhance recommendation quality. However, existing negative sampling techniques often struggle to effectively utilize the multi-modal data, leading to suboptimal performance. In this paper, we identify two key challenges in negative sampling for MMRS: (1) producing cohesive negative samples contrasting with positive samples and (2) maintaining a balanced influence across different modalities. To address these challenges, we propose NegGen, a novel framework that utilizes multi-modal large language models (MLLMs) to generate balanced and contrastive negative samples. We design three different prompt templates to enable NegGen to analyze and manipulate item attributes across multiple modalities, and then generate negative samples that introduce better supervision signals and ensure modality balance. Furthermore, NegGen employs a causal learning module to disentangle the effect of intervened key features and irrelevant item attributes, enabling fine-grained learning of user preferences. Extensive experiments on real-world datasets demonstrate the superior performance of NegGen compared to state-of-the-art methods in both negative sampling and multi-modal recommendation.

Recent advancements in visual multi-task learning (MTL) have sparked significant interest. However, existing dense prediction MTL methods predominantly rely on single-modality image data, limiting their performance due to the absence of complementary knowledge from other modalities. Additionally, different dense tasks exhibit heterogeneous preferences during information decoding, posing a critical challenge in effectively allocating multi-scale encoded features. To address these limitations, we propose CLIP-MT, a Multi-Modal Knowledge-Driven Adaptive Scale Feature Allocation for Multi-Task Dense Prediction. Specifically, to enrich task-shared image features with multi-modal knowledge, we introduce a novel CLIP-Guided Global Feature Enhancer (CGGF), which leverages aligned text-image information to augment object-level representations through a dual-path feature fusion architecture. Furthermore, to tackle the task-specific scale preference problem, we design an Adaptive Scale Selection Gate (ASSG), a learnable gating mechanism that dynamically selects high- or low-scale features based on task-specific demands. Finally, we integrate multi-modal and multi-scale information through a Task-Aware Feature Fusion Module (TAFF). Extensive experiments on the NYUDv2 and PASCAL-Context datasets demonstrate that CLIP-MT achieves state-of-the-art performance, outperforming existing methods across multiple dense prediction tasks.

CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hindering generalization ability to unseen anatomies and lesions. To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. This dataset serves two key purposes: (1) enabling robust training of deep learning models on extensive, heterogeneous data, and (2) facilitating rigorous evaluation of model generalization for CT reconstruction. We further establish a strong baseline solution that outperforms prior approaches under these challenging conditions. Our results demonstrate that: (1) a comprehensive dataset helps improve the generalization capability of models, and (2) optimization-based methods offer enhanced robustness for unseen anatomies. The MORE dataset is freely accessible under CC-BY-NC 4.0 at our project page https://more-med.github.io/.

Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence score of output proposals and bounding boxes provide insight into the scale and density of the crowd. To leverage these underutilized features, we propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models. This module uses a mixed 2D-1D compression technique to refine the output features and obtain the spatial and numerical distribution of crowd-specific information. Based on these features, we further propose region-adaptive NMS thresholds and a decouple-then-align paradigm that address the major limitations of detection-based methods. Our extensive evaluations on various crowd analysis tasks, including crowd counting, localization, and detection, demonstrate the effectiveness of utilizing output features and the potential of detection-based methods in crowd analysis. Our code is available at https://github.com/wskingdom/Crowd-Hat.