The rise of generative models has transformed image generation and editing, enabling high-quality, user-guided outputs. Iterative face editing, essential for applications like virtual makeup and entertainment, allows users to refine images progressively. However, this process often leads to artifact accumulation, semantic inconsistency, and quality degradation over multiple edits. Existing methods, while effective in single-step modifications, struggle with sequential edits. To robustly maintain fidelity and consistency in iterative face editing across multiple sessions, we propose IterDiff, a training-free framework leveraging diffusion models with a novel Training-Free Feature Preservation (TF2P) approach to tackle these challenges by storing and retrieving key-value (KV) pairs from self-attention layers. Additionally, we further improve its efficiency and feasibility by Efficient CLIP-guided Memory Bank (ECMB). Experiments on the proposed benchmark show that IterDiff excels in prompt alignment, content consistency, and image quality, providing a robust solution for iterative facial attribute editing. Code, dataset and supplementary materials are available at https://github.com/david20571015/IterDiff.

Video Individual Counting (VIC), which seeks to count unique individuals across video sequences without duplication, has broader applications than traditional Video Crowd Counting (VCC), including urban planning, event management, and safety monitoring. However, although current VIC approaches have demonstrated strong capabilities, their reliance on identity-level or group-level annotations necessitates substantial labeling effort and expense. To reduce the high costs of manual annotation, we introduce VIC-SSL, a novel self-supervised learning approach that utilizes unlabeled data along with the innovative feature-level augmentation technique called Foreground-driven ShiftMix (F-ShiftMix). By blending and shifting in the feature space rather than the image space, F-ShiftMix generates realistic crowd motion without explicit annotations, while preserving global semantic coherence. Furthermore, VIC-SSL integrates the Cost-guided Flow Prompt (CFP) and the Distinction-aware Cross-Attention (DCA) to enhance flow-aware localization and inter-frame correspondence learning. Our extensive experiments across three datasets, including SenseCrowd, CroHD, and CARLA, demonstrate that VIC-SSL substantially outperforms existing methods, achieving state-of-the-art results with significantly reduced data requirements. These results showcase VIC-SSL's potential to dramatically lower annotation costs and improve the deployment feasibility of VIC systems in complex scenarios. The project website is available at https://leohuang0511.github.io/vic-ssl.

Long-term multi-animal tracking in densely group-housed agricultural settings is critical for automated behavior monitoring and early anomaly detection in precision livestock farming. However, it poses significant challenges due to persistent occlusions from feeders and water dispensers, high inter-individual appearance similarity, and drastic visual changes across day and night cycles. Existing multi-object tracking datasets rarely capture the combined difficulty of these real-world conditions. To address this, we introduce OinkTrack, a large-scale benchmark for continuous multi-pig tracking in commercial farm environments. The dataset comprises over five hours of annotated video across sixteen sequences, covering day, night, night-to-day, and day-to-night transitions. Each sequence ranges from one minute to one hour, featuring an average of thirty-six pigs per frame. In total, OinkTrack provides 573,700 bounding boxes linked to 574 consistent pig identities. It enables detailed behavior analysis under varying lighting and crowding conditions. We describe the data collection and annotation process, present statistical insights into tracking difficulty, and benchmark 11 state-of-the-art tracking methods. OinkTrack provides a robust foundation for developing long-term tracking models and supports downstream applications such as individual activity profiling and early detection of abnormal behavior in real-world, high-density animal populations. The complete dataset and supplementary materials are publicly accessible at https://leohuang0511.github.io/oinktrack-page.

Crowd counting is currently applied in many areas, such as transportation hubs and streets. However, most of the research still focuses on counting the number of people in a single image, and there is little research on solving the problem of calculating the number of non-repeated people in a video segment. Currently, multiple object tracking is mainly relied upon for video counting, but this method is not suitable for situations where the crowd density is too high. Therefore, we propose a Flow Mask Integration Deformable Convolution network (FMDC) combined with Inter-Frame Head Contrastive Learning (IFHC) to predict the situation of people entering and exiting the screen in a density-based manner. We verify that our proposed method is highly effective in densely populated situations and diverse scenes, and the experimental results show that our proposed method surpasses existing methods.