Face clustering aims to group unlabeled face images into identity-consistent clusters and is essential for large-scale face retrieval and dataset construction. However, existing graph-based methods often suffer from false positive connections and limited recall in complex feature spaces. This paper introduces a robust face clustering framework that addresses these challenges through two key components. First, the proposed local identity relation estimation network (LIRENet) verifies edge reliability by modeling both local context and identity-level interactions. Second, a neighborhood edge extension module adaptively expands the graph using shared neighbor constraints and similarity thresholds. The combination of these components enhances connectivity prediction while preserving cluster quality. Experimental results on MS-Celeb-1M, IJB-B, and DeepFashion demonstrate consistent improvements over previous state-of-the-art methods on both closed- and open-set benchmarks.
In the field of computer vision, accurate estimation of 3D human poses and shapes plays a vital role in applications such as motion analysis, virtual try-on, and human-computer interaction. While most existing methods rely on monocular RGB images and deep learning regression networks, they often suffer from ambiguities caused by occlusions, self-similasrity, and missing visual cues. Leveraging publicly available multi-view image datasets offers an effective way to mitigate these limitations and improve reconstruction accuracy.In this work, we propose a novel framework for 3D human reconstruction that jointly lever-ages multi-view consistency and iterative refinement of SMPL parameters. Specifically, our method takes images from multiple viewpoints as input, extracts SMPL pose and shape parameters from each view using a CNN-based regressor, and integrates them through a multi-view parameter fusion strategy. To further refine the fused parameters, we introduce an Online Evolutive Learning(OEL) mechanism, which applies a SMPLify-inspired optimization process to iteratively minimize a reprojection-based evaluation loss without requiring ground-truth 3D annotations. This iterative optimization loop progressively enhances structural plausibility and alignment consistency across views. The im-proved parameters are then used to optimize the CNN regressor in an online evolutive manner, forming a feedback loop that continuously improves the model generalization ability.
Image inpainting, the task of synthesizing visually plausible content in missing or occluded regions of an image, has seen substantial progress in the context of 2D real-world imagery. However, existing methods often struggle to generalize to 3D virtual environments due to fundamental differences in geometric structure, rendering pipelines, and the diversity of visual styles and abstraction levels inherent in virtual scenes. To address these limitations, we propose a novel inpainting framework specifically adapted for 3D virtual environments. Our approach builds upon conventional inpainting architectures but introduces domain-specific enhancements to better capture the structural and semantic characteristics of virtual scenes. We evaluate our method across multiple widely-used 3D virtual environment datasets and additional in-the-wild virtual samples to assess its generalizability. Experimental results demonstrate that our model not only achieves competitive performance in filling missing regions but also enables practical applications such as scene editing and content creation, thereby validating the effectiveness and utility of the proposed framework.
Low-light photography is one of the most common use cases in the current smartphone industry. Today, multiple smartphones offer some form of night-mode photography that allows images to be captured in low-light conditions without the use of flash. However, most solutions that exist today depend on increasing exposure times and capturing multiple frames, which leads to long waiting times along with other issues such as motion blur. While other deep learning solutions exist, they are too heavy to be integrated in a smartphone. In this paper, we propose a lightweight low-light enhancement method that can work with a single frame, a custom loss function tailored for low-light enhancement and a multi-stage training process to enhance our model’s quality. Our testing demonstrates that our strategy can yield outputs that are comparable to SOTA techniques while requiring a significantly lesser latency and memory footprint, making them ideal for smartphones.
Video object detection (VOD) is vital in edge intelligence applications such as surveillance, autonomous systems, and wearable devices. Although high-performance still-image detectors such as YOLO are commonly employed in VOD tasks, applying detection to every video frame has redundant computation and a higher energy cost, making such a solution less attractive for resource-constrained platforms. This paper proposes MA-YOLO (Motion-Assisted YOLO), a lightweight VOD framework tailored for edge environments. Instead of inferring on every frame, MA-YOLO executes complete YOLO detection only on sparse keyframes and propagates the results to intermediate frames using motion information derived from precomputed H. 264 motion vectors and geometric offsets from reference detections. We introduce a lightweight, XGBoost-based decision module tailored to each geometric offset regression, realizing efficient detection propagation from the keyframes to non-keyframes. Experiments on the ImageNet VID dataset demonstrate that MA-YOLO reduces inference cost while maintaining competitive accuracy, offering a practical and efficient solution for edge-based video analysis.
