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.

This paper analyzes the impact of causal manner in the text encoder of text-to-image (T2I) diffusion models, which can lead to information bias and loss. Previous works have focused on addressing the issues through the denoising process. However, there is no research discussing how text embedding contributes to T2I models, especially when generating more than one object. In this paper, we share a comprehensive analysis of text embedding: i) how text embedding contributes to the generated images and ii) why information gets lost and biases towards the first-mentioned object. Accordingly, we propose a simple but effective text embedding balance optimization method, which is training-free, with an improvement of 125.42\% on information balance in stable diffusion. Furthermore, we propose a new automatic evaluation metric that quantifies information loss more accurately than existing methods, achieving 81\% concordance with human assessments. This metric effectively measures the presence and accuracy of objects, addressing the limitations of current distribution scores like CLIP's text-image similarities.

As makeup has been widely-adopted for beautification, finding suitable makeup by virtual makeup applications becomes popular. Therefore, a recent line of studies proposes to transfer the makeup from a given reference makeup image to the source non-makeup one. However, it is still challenging due to the massive number of makeup combinations. To facilitate on-demand makeup transfer, in this work, we propose BeautyGlow that decompose the latent vectors of face images derived from the Glow model into makeup and non-makeup latent vectors. Since there is no paired dataset, we formulate a new loss function to guide the decomposition. Afterward, the non-makeup latent vector of a source image and makeup latent vector of a reference image and are effectively combined and revert back to the image domain to derive the results. Experimental results show that the transfer quality of BeautyGlow is comparable to the state-of-the-art methods, while the unique ability to manipulate latent vectors allows BeautyGlow to realize on-demand makeup transfer.