This paper investigates a fundamental yet underexplored issue in Salient Object Detection (SOD): the size-invariant property for evaluation protocols, particularly in scenarios when multiple salient objects of significantly different sizes appear within a single image. We first present a novel perspective to expose the inherent size sensitivity of existing widely used SOD metrics. Through careful theoretical derivations, we show that the evaluation outcome of an image under current SOD metrics can be essentially decomposed into a sum of several separable terms, with the contribution of each term being directly proportional to its corresponding region size. Consequently, the prediction errors would be dominated by the larger regions, while smaller yet potentially more semantically important objects are often overlooked, leading to biased performance assessments and practical degradation. To address this challenge, a generic Size-Invariant Evaluation (SIEva) framework is proposed. The core idea is to evaluate each separable component individually and then aggregate the results, thereby effectively mitigating the impact of size imbalance across objects. Building upon this, we further develop a dedicated optimization framework (SIOpt), which adheres to the size-invariant principle and significantly enhances the detection of salient objects across a broad range of sizes. Notably, SIOpt is model-agnostic and can be seamlessly integrated with a wide range of SOD backbones. Theoretically, we also present generalization analysis of SOD methods and provide evidence supporting the validity of our new evaluation protocols. Finally, comprehensive experiments speak to the efficacy of our proposed approach.

Noise synthesis is a promising solution for addressing the data shortage problem in data-driven low-light RAW image denoising. However, accurate noise synthesis methods often necessitate labor-intensive calibration and profiling procedures during preparation, preventing them from landing to practice at scale. This work introduces a practically simple noise synthesis pipeline based on detailed analyses of noise properties and extensive justification of widespread techniques. Compared to other approaches, our proposed pipeline eliminates the cumbersome system gain calibration and signal-independent noise profiling steps, reducing the preparation time for noise synthesis from days to hours. Meanwhile, our method exhibits strong denoising performance, showing an up to 0.54dB PSNR improvement over the current state-of-the-art noise synthesis technique. Code is released at https://github.com/SonyResearch/raw_image_denoising

Concept erasing has recently emerged as an effective paradigm to prevent text-to-image diffusion models from generating visually undesirable or even harmful content. However, current removal methods heavily rely on manually crafted text prompts, making it challenging to achieve a high erasure (efficacy) while minimizing the impact on other benign concepts (usability), as illustrated in Fig.1. In this paper, we attribute the limitations to the inherent gap between the text and image modalities, which makes it hard to transfer the intricately entangled concept knowledge from text prompts to the image generation process. To address this, we propose a novel solution by directly integrating visual supervision into the erasure process, introducing the first text-image Collaborative Concept Erasing (Co-Erasing) framework. Specifically, Co-Erasing describes the concept jointly by text prompts and the corresponding undesirable images induced by the prompts, and then reduces the generating probability of the target concept through negative guidance. This approach effectively bypasses the knowledge gap between text and image, significantly enhancing erasure efficacy. Additionally, we design a text-guided image concept refinement strategy that directs the model to focus on visual features most relevant to the specified text concept, minimizing disruption to other benign concepts. Finally, comprehensive experiments suggest that Co-Erasing outperforms state-of-the-art erasure approaches significantly with a better trade-off between efficacy and usability.

Facial micro-expression recognition (MER) is a challenging problem, due to transient and subtle micro-expression (ME) actions. Most existing methods depend on hand-crafted features, key frames like onset, apex, and offset frames, or deep networks limited by small-scale and low-diversity datasets. In this paper, we propose an end-to-end micro-action-aware deep learning framework with advantages from transformer, graph convolution, and vanilla convolution. In particular, we propose a novel F5C block composed of fully-connected convolution and channel correspondence convolution to directly extract local-global features from a sequence of raw frames, without the prior knowledge of key frames. The transformer-style fully-connected convolution is proposed to extract local features while maintaining global receptive fields, and the graph-style channel correspondence convolution is introduced to model the correlations among feature patterns. Moreover, MER, optical flow estimation, and facial landmark detection are jointly trained by sharing the local-global features. The two latter tasks contribute to capturing facial subtle action information for MER, which can alleviate the impact of insufficient training data. Extensive experiments demonstrate that our framework (i) outperforms the state-of-the-art MER methods on CASME II, SAMM, and SMIC benchmarks, (ii) works well for optical flow estimation and facial landmark detection, and (iii) can capture facial subtle muscle actions in local regions associated with MEs.

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method.

As a special sensor that implicitly provides ordinal depth information, dual-pixel (DP) appears to be beneficial for various tasks such as defocus deblurring and monocular depth estimation. Recent advances in data-driven dual-pixel (DP) research are bottlenecked by the difficulties in reaching large-scale DP datasets, and a photorealistic image synthesis approach appears to be a credible solution. To benchmark the accuracy of various existing DP image simulators and facilitate data-driven DP image synthesis, this work presents a real-world DP dataset consisting of approximately 5000 high-quality pairs of sharp images, DP defocus blur images, detailed imaging parameters, and accurate depth maps. Based on this large-scale dataset, we also propose a holistic data-driven framework to synthesize photorealistic DP images, where a neural network replaces conventional handcrafted imaging models. Experiments show that our neural DP simulator can generate more photorealistic DP images than existing state-of-the-art methods and effectively benefit data-driven DP-related tasks. Our code and dataset are released at https://github.com/SILI1994/Dual-Pixel-Simulator.

BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.

We consider the shuffled linear regression problem where the correspondences between covariates and responses are unknown. While the existing formulation assumes an ideal underlying bijection in which all pieces of data should match, such an assumption barely holds in real-world applications due to either missing data or outliers. Therefore, in this work, we generalize the formulation of shuffled linear regression to a broader range of conditions where only part of the data should correspond. Moreover, we present a remarkably simple yet effective optimization algorithm with guaranteed global convergence. Distinct tasks validate the effectiveness of the proposed method.1

We consider the deep point cloud analysis tasks where the inputs of the networks are randomly rotated. Recent progress in rotation-invariant point cloud analysis is mainly driven by converting point clouds into their respective canonical poses, and principal component analysis (PCA) is a practical tool to achieve this. Due to the imperfect alignment of PCA, most of the current works are devoted to developing powerful network structures and features to overcome this deficiency, without thoroughly analyzing the PCA-based canonical poses themselves. In this work, we present a detailed study w.r.t. the PCA-based canonical poses of point clouds. Our investigation reveals that the ambiguity problem associated with the PCA-based canonical poses is handled insufficiently in some recent works. To this end, we develop a simple pose selector module for disambiguation, which presents noticeable enhancement (i.e., 5.3% classification accuracy) over state-of-the-art approaches on the challenging real-world dataset.1

3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In this work, we propose an effective and efficient approach at a low cost. First, we obtain panoramic videos with four normal cameras. Then, we transform human locations from a 2D panoramic image coordinate to a 3D panoramic camera coordinate using camera geometry and human bio-metric property (i.e., height). Finally, we generate 3D tracklets by associating human appearance and 3D trajectory. We verify the effectiveness of our method on three datasets including a new one built by us, in terms of 3D single-view multi-person localization, 3D single-view multi-person tracking, and 3D panoramic multi-person localization and tracking. Our code and dataset are available at https://github.com/fandulu/MPLT.

In this work we propose a novel approach to remove undesired objects from RGB-D sequences captured with freely moving cameras, which enables static 3D reconstruction. Our method jointly uses existing information from multiple frames as well as generates new one via inpainting techniques. We use balanced rules to select source frames; local homography based image warping method for alignment and Markov random field (MRF) based approach for combining existing information. For the left holes, we employ exemplar based multi-view inpainting method to deal with the color image and coherently use it as guidance to complete the depth correspondence. Experiments show that our approach is qualified for removing the undesired objects and inpainting the holes.

Tangible interaction links digital data with physical forms to support embodied use. Puzzle pieces, which their inherent physical syntax of connectable elements, provide a powerful and expressive metaphor on which to construct such tangible systems. Prior work has explored this potential in the domain of edutainment systems for children aimed at tasks such as learning logic, programming or organizational skills. Although this work is promising, it has largely focused on relatively advanced concepts and children of ages 7-12 years. The work presented in this paper adopts the same perspective but focuses on young children (5 and under) and a simpler range of concepts relating to the clustering and manipulation of data. To achieve this it presents the design (including results from a series of six formative field studies) and implementation of the Digital Dream Lab tabletop puzzle block system. This system, intended for installation in a museum, engages young children (aged 4-5) to explore simple programmatic concepts and the link between the physical and virtual world. The paper closes with design recommendations of future work targeting this goal, setting and age group.