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.

Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design, yet current diffusion models struggle with nuanced and compound color terms (e.g., Tiffany blue, baby pink), often producing images that are misaligned with human intent. Existing approaches rely on cross-attention manipulation, reference images, or fine-tuning but fail to systematically resolve ambiguous color descriptions. To precisely render colors under prompt ambiguity, we propose a training-free framework that enhances color fidelity by leveraging a large language model (LLM) to disambiguate color-related prompts and guiding color blending operations directly in the text embedding space. Our method first employs a large language model (LLM) to resolve ambiguous color terms in the text prompt, and then refines the text embeddings based on the spatial relationships of the resulting color terms in the CIELab color space. Unlike prior methods, our approach improves color accuracy without requiring additional training or external reference images. Experimental results demonstrate that our framework improves color alignment without compromising image quality, bridging the gap between text semantics and visual generation. All supplementary materials are available at https://Sung-Lin.github.io/TintBench/.

Common illumination sources like sunlight or artificial light may introduce hidden vulnerabilities to AI systems. Our paper delves into these potential threats, offering a novel approach to simulate varying light conditions, including sunlight, headlights, and flashlight illuminations. Moreover, unlike typical physical adversarial attacks requiring conspicuous alterations, our method utilizes a model-agnostic black-box attack integrated with the Zeroth Order Optimization (ZOO) algorithm to identify deceptive patterns in a physically-applicable space. Consequently, attackers can recreate these simulated conditions, deceiving machine learning models with seemingly natural light. Empirical results demonstrate the efficacy of our method, misleading models trained on the GTSRB and LISA datasets under natural-like physical environments with an attack success rate exceeding 70% across all digital datasets, and remaining effective against all evaluated real-world traffic signs. Importantly, after adversarial training using samples generated from our approach, models showcase enhanced robustness, underscoring the dual value of our work in both identifying and mitigating potential threats.1