International Journal of Computer Vision最新文献

In deep learning-based no-reference image quality assessment (NR-IQA) methods, the absence of reference images limits their ability to assess content fidelity, making it difficult to distinguish between original content and distortions that degrade quality. To address this issue, we propose a quality adversarial learning framework emphasizing both content fidelity and prediction accuracy. The main contributions of this study are as follows: First, we investigate the importance of content fidelity, especially in no-reference scenarios. Second, we propose a quality adversarial learning framework that dynamically adapts and refines the image quality assessment process on the basis of the quality optimization results. The framework generates adversarial samples for the quality prediction model, and simultaneously, the quality prediction model optimizes the quality prediction model by using these adversarial samples to maintain fidelity and improve accuracy. Finally, we demonstrate that by employing the quality prediction model as a loss function for image quality optimization, our framework effectively reduces the generation of artifacts, highlighting its superior ability to preserve content fidelity. The experimental results demonstrate the validity of our method compared with state-of-the-art NR-IQA methods. The code is publicly available at the following website: https://github.com/Land5cape/QAL-IQA.

Pathological diagnosis is the gold standard for tumor diagnosis, and nucleus instance segmentation is a key step in digital pathology analysis and pathological diagnosis. However, the computational efficiency of the model and the treatment of overlapping targets are the major challenges in the studies of this problem. To this end, a neural network model RepSNet was designed based on a nucleus boundary regression and a structural re-parameterization scheme for segmenting and classifying the nuclei in H&E-stained histopathological images. First, RepSNet estimates the boundary position information (BPI) of the parent nucleus for each pixel. The BPI estimation incorporates the local information of the pixel and the contextual information of the parent nucleus. Then, the nucleus boundary is estimated by aggregating the BPIs from a series of pixels using a proposed boundary voting mechanism (BVM), and the instance segmentation results are computed from the estimated nucleus boundary using a connected component analysis procedure. The BVM intrinsically achieves a kind of synergistic belief enhancement among the BPIs from various pixels. Therefore, different from the methods available in literature that obtain nucleus boundaries based on a direct pixel recognition scheme, RepSNet computes its boundary decisions based on some guidances from macroscopic information using an integration mechanism. In addition, RepSNet employs a re-parametrizable encoder-decoder structure. This model can not only aggregate features from some receptive fields with various scales which helps segmentation accuracy improvement, but also reduce the parameter amount and computational burdens in the model inference phase through the structural re-parameterization technique. In the experimental comparisons and evaluations on the Lizard dataset, RepSNet demonstrated superior segmentation accuracy and inference speed compared to several typical benchmark models. The experimental code, dataset splitting configuration and the pre-trained model were released at https://github.com/luckyrz0/RepSNet.

Given only a set of images, neural implicit surface representation has shown its capability in 3D surface reconstruction. However, as the nature of per-scene optimization is based on the volumetric rendering of color, previous neural implicit surface reconstruction methods usually fail in the low-textured regions, including floors, walls, etc., which commonly exist for indoor scenes. Being aware of the fact that these low-textured regions usually correspond to planes, without introducing additional ground-truth supervisory signals or making additional assumptions about the room layout, we propose to leverage a novel Pseudo-plane regularized Signed Distance Field (PPlaneSDF) for indoor scene reconstruction. Specifically, we consider adjacent pixels with similar colors to be on the same pseudo-planes. The plane parameters are then estimated on the fly during training by an efficient and effective two-step scheme. Then the signed distances of the points on the planes are regularized by the estimated plane parameters in the training phase. As the unsupervised plane segments are usually noisy and inaccurate, we propose to assign different weights to the sampled points on the plane in plane estimation as well as the regularization loss. The weights come by fusing the plane segments from different views. As the sampled rays in the planar regions are redundant, leading to inefficient training, we further propose a keypoint-guided rays sampling strategy that attends to the informative textured regions with large color variations, and the implicit network gets a better reconstruction, compared with the original uniform ray sampling strategy. Experiments show that our PPlaneSDF achieves competitive reconstruction performance in Manhattan scenes. Further, as we do not introduce any additional room layout assumption, our PPlaneSDF generalizes well to the reconstruction of non-Manhattan scenes.

Addressing the dynamic nature of long-term person re-identification (LT-reID) amid varying clothing conditions necessitates a departure from conventional methods. Traditional LT-reID strategies, mainly biometrics-based and data adaptation-based, each have their pitfalls. The former falters in environments lacking high-quality biometric data, while the latter loses efficacy with minimal or subtle clothing changes. To overcome these obstacles, we propose the clothing status-aware feature regularization network (CSFRNet). This novel approach seamlessly incorporates clothing status awareness into the feature learning process, significantly enhancing the adaptability and accuracy of LT-reID systems where clothing can either change completely, partially, or not at all over time, without the need for explicit clothing labels. The versatility of our CSFRNet is showcased on diverse LT-reID benchmarks, including Celeb-reID, Celeb-reID-light, PRCC, DeepChange, and LTCC, marking a significant advancement in the field by addressing the real-world variability of clothing in LT-reID scenarios.

Clipart, a pre-made graphic art form, offers a convenient and efficient way of illustrating visual content. Traditional workflows to convert static clipart images into motion sequences are laborious and time-consuming, involving numerous intricate steps like rigging, key animation and in-betweening. Recent advancements in text-to-video generation hold great potential in resolving this problem. Nevertheless, direct application of text-to-video generation models often struggles to retain the visual identity of clipart images or generate cartoon-style motions, resulting in unsatisfactory animation outcomes. In this paper, we introduce AniClipart, a system that transforms static clipart images into high-quality motion sequences guided by text-to-video priors. To generate cartoon-style and smooth motion, we first define Bézier curves over keypoints of the clipart image as a form of motion regularization. We then align the motion trajectories of the keypoints with the provided text prompt by optimizing the Video Score Distillation Sampling (VSDS) loss, which encodes adequate knowledge of natural motion within a pretrained text-to-video diffusion model. With a differentiable As-Rigid-As-Possible shape deformation algorithm, our method can be end-to-end optimized while maintaining deformation rigidity. Experimental results show that the proposed AniClipart consistently outperforms existing image-to-video generation models, in terms of text-video alignment, visual identity preservation, and motion consistency. Furthermore, we showcase the versatility of AniClipart by adapting it to generate a broader array of animation formats, such as layered animation, which allows topological changes.

Modern deep learning systems are data-hungry. Learning with web data is one of the feasible solutions, but will introduce label noise inevitably, which can hinder the performance of deep neural networks. Sample selection is an effective way to deal with label noise. The key is to separate clean samples based on some criterion. Previous methods pay more attention to the small loss criterion where small-loss samples are regarded as clean ones. Nevertheless, such a strategy relies on the learning dynamics of each data instance. Some noisy samples are still memorized due to frequently occurring corrupted learning patterns. To tackle this problem, a training-free surrogate model is preferred, freeing from the effect of memorization. In this work, we propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. CLIP brings external knowledge to facilitate the selection of clean samples with its ability of text-image alignment. Furthermore, a margin adaptive loss is designed to regularize the selection bias introduced by CLIP, providing robustness to label noise. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets. Our method achieves significant improvement without CLIP involved during the inference stage.

Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency between the feature space and the pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to quantify the inconsistency between the pseudo-label space and the feature space, subsequently minimizing it. The proposed MULT ensures that the generated pseudo-labels maintain alignment across modalities while upholding structural consistency within intra-modality. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the side effects of noisy pseudo-labels while simultaneously aligning different modalities, coupled with an Alternative Modality-Invariant Representation Learning (AMIRL) framework. Experiments demonstrate that our proposed method outperforms existing state-of-the-art USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. Code is available at https://github.com/FranklinLingfeng/code_for_MULT.

The generation of textured mesh is crucial for computer graphics and virtual content creation. However, current generative models often struggle with challenges such as irregular mesh structures and inconsistencies in multi-view textures. In this study, we present a unified framework for both geometry generation and texture generation, utilizing a novel sparse latent point diffusion model that specifically addresses the geometric aspects of models. Our approach employs point clouds as an efficient intermediate representation, encoding them into sparse latent points with semantically meaningful features for precise geometric control. While the sparse latent points facilitate a high-level control over the geometry, shaping the overall structure and fine details of the meshes, this control does not extend to textures. To address this, we propose a separate texture generation process that integrates multi-view priors post-geometry generation, effectively resolving the issue of multi-view texture inconsistency. This process ensures the production of coherent and high-quality textures that complement the precisely generated meshes, thereby creating visually appealing and detailed models. Our framework distinctively separates the control mechanisms for geometry and texture, leading to significant improvements in the generation of complex, textured 3D content. Evaluations on the ShapeNet dataset for geometry and the Objaverse dataset for textures demonstrate that our model surpasses existing methods in terms of geometric quality, control, and the generation of coherent, high-quality textures.

In recent years, numerous ideas have emerged for designing a mutually reinforcing mechanism or extra stages for the image fusion task, ignoring the inevitable gaps between different vision tasks and the computational burden. We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for fusion tasks. In particular, our booster is based on the divide-and-conquer strategy controlled by an information probe. The booster is composed of three building blocks: the probe units, the booster layer, and the assembling module. Given the result produced by a backbone method, the probe units assess the fused image and divide the results according to their information content. This is instrumental in identifying missing information, as a step to its recovery. The recovery of the degraded components along with the fusion guidance are the role of the booster layer. Lastly, the assembling module is responsible for piecing these advanced components together to deliver the output. We use concise reconstruction loss functions in conjunction with lightweight autoencoder models to formulate the learning task, with marginal computational complexity increase. The experimental results obtained in various fusion missions, as well as downstream detection tasks, consistently demonstrate that the proposed FusionBooster significantly improves the performance. Our code will be publicly available at https://github.com/AWCXV/FusionBooster.

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously (a) accomplish the synthesis of visually realistic and temporally coherent videos while (b) preserving the strong creative generation nature of the pre-trained T2I model. To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. Our key insights are two-fold: (1) We reveal that the incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data. (2) Additionally, we validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes. To enhance the performance of LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M, consisting of 25 million text-video pairs that prioritize quality, diversity, and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves state-of-the-art performance both quantitatively and qualitatively. Furthermore, we showcase the versatility of pre-trained LaVie models in various long video generation and personalized video synthesis applications. Project page: https://github.com/Vchitect/LaVie/.