Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set of 3,799,695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR. (The PPIRD dataset is available at: \href{https://loslorien.github.io/idama-project/}{https://loslorien.github.io/idama-project/})

Recently, a decoupled framework for learning-based image compression has been proposed and adopted into the JPEG AI image coding standard developed by ISO/IEC WG1. The decoupled structure disentangles the sample reconstruction process and the entropy decoding process, making the decoding extremely fast. The corresponding techniques constitute the essential parts of the JPEG AI verification model software. However, its analysis transform and synthesis transform are relatively simple, which are built with stacked convolution layers, thereby may lack the capability to interpret data correlations. In this work, we enhance the transform networks by introducing the non-local attention mechanism, which has proven efficient in image compression tasks. The proposed framework thus shares the merits of the fast decoding from the decoupled architecture and the strong transform capabilities from the non-local attention, making it a stronger candidate for practical end-to-end image codec deployment. Experimental results on the Kodak test set and JPEG AI CfP test set show that our method achieves better BDRate performance compared to the original Decoupled-anchor and significantly faster decoding speed compared to NIC. The proposed solution has been adopted by the IEEE 1857.11 Working Subgroup (1857.11 WSG) in developing neural network-based image coding standards in the 10th Meeting.

This paper presents HandFi, which constructs hand skeletons with practical WiFi devices. Unlike previous WiFi hand sensing systems that primarily employ predefined gestures for pattern matching, by constructing the hand skeleton, HandFi can enable a variety of downstream WiFi-based hand sensing applications in gaming, healthcare, and smart homes. Deriving the skeleton from WiFi signals is challenging, especially because the palm is a dominant reflector compared with fingers. HandFi develops a novel multi-task learning neural network with a series of customized loss functions to capture the low-level hand information from WiFi signals. During offline training, HandFi takes raw WiFi signals as input and uses the leap motion to provide supervision. During online use, only with commercial WiFi, HandFi is capable of producing 2D hand masks as well as 3D hand poses. We demonstrate that HandFi can serve as a foundation model to enable developers to build various applications such as finger tracking and sign language recognition, and outperform existing WiFi-based solutions. Artifacts can be found: https://github.com/SIJIEJI/HandFi

Excellent performance has been achieved on instance segmentation but the quality on the boundary area remains unsatisfactory, which leads to a rising attention on boundary refinement. For practical use, an ideal post-processing refinement scheme are required to be accurate, generic and efficient. However, most of existing approaches propose pixel-wise refinement, which either introduce a massive computation cost or design specifically for different backbone models. Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas. In this paper, we propose an efficient contour-based boundary refinement approach, named SharpContour, to tackle the segmentation of boundary area. We design a novel contour evolution process together with an Instance-aware Point Classifier. Our method deforms the contour iteratively by updating offsets in a discrete manner. Differing from existing contour evolution methods, SharpContour estimates each offset more independently so that it predicts much sharper and accurate contours. Notably, our method is generic to seamlessly work with diverse existing models with a small computational cost. Experiments show that SharpContour achieves competitive gains whilst preserving high efficiency.