Despite considerable progress, the advancement of Panoptic Narrative Grounding (PNG) remains hindered by costly annotations. In this paper, we introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG) learning scheme, capitalizing on a smaller set of labeled image-text pairs and a larger set of unlabeled pairs to achieve competitive performance. Unlike visual segmentation tasks, PNG involves one pixel belonging to multiple open-ended nouns. As a result, existing multi-class based semi-supervised segmentation frameworks cannot be directly applied to this task. To address this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and data augmentation to determine the optimal generic configuration for the SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels, respectively. We conduct extensive experiments on PNG datasets, with our SS-PNG-NW+ demonstrating promising results comparable to fully-supervised models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0.8% and 1.1% respectively. This highlights the effectiveness of our proposed SS-PNG-NW+ in overcoming the challenges posed by limited annotations and enhancing the applicability of PNG tasks. The source code is available at https://github.com/nini0919/SSPNG.
论文检索
输入标题、作者或关键词，从 5,999 篇学术成果中精准定位
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences
PDF ↗Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pretrained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pretrained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pretrained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU.
The bottleneck of visual domain adaptation always lies in the learning of domain invariant representations. In this paper, we present a simple but effective technique named Adaptive Feature Swapping for learning domain invariant features in Unsupervised Domain Adaptation (UDA). Adaptive Feature Swapping aims to select semantically irrelevant features from labeled source data and unlabeled target data and swap these features with each other. Then the merged representations are also utilized for training with prediction consistency constraints. In this way, the model is encouraged to learn representations that are robust to domain-specific information. We develop two swapping strategies including channel swapping and spatial swapping. The former encourages the model to squeeze redundancy out of features and pay more attention to semantic information. The latter motivates the model to be robust to the background and focus on objects. We conduct experiments on object recognition and semantic segmentation in UDA setting and the results show that Adaptive Feature Swapping can promote various existing UDA methods. Our codes are publicly available at https://github.com/junbaoZHUO/AFS.
Cartoon parsing, the task of segmenting constituent parts such as heads, arms, and legs of cartoon characters, holds substantial significance for applications in the animation industry and emerging metaverse. Nonetheless, this domain presents considerable challenges stemming from complex visual appearances, irregular structures, abstract drawing styles, among other factors. In this paper, a novel Cartoon Parsing Network (CPNet) is introduced to address these challenges. CPNet skillfully leverages the spatial and semantic correlations of pixels to discern intricate and visually akin appearances. Furthermore, it employs both local and global correlations of constituent parts to differentiate irregular and abstract body sections. Specifically, the pixels of the cartoon image are interconnected by capitalizing on the spatial and semantic correlations. To this end, a center point predictor, working in tandem with a pixel-aware attention, facilitates the exploration of pixel-level correlation learning. Additionally, the various constituent parts are meticulously organized to resonate with the intrinsic physiological structure of a cartoon character. The character's graph structure is assembled and analyzed by an edge-aware graph neural network, thereby linking adjacent parts and assimilating local correlations. A part-guided non-local attention mechanism is fashioned to correlate individual parts with the entire body, thereby modeling global connections. In addition, a new dataset named CartoonSet is curated and annotated explicitly for cartoon parsing. Experiments carried out on both cartoon parsing and human parsing datasets yield compelling results, thereby attesting to the efficacy and innovativeness of the proposed method.
Composite images are generated by combining two or more different photographs, and their content is typically heterogeneous. However, existing unimodal visual redundancy prediction methods are difficult to accurately model the complex characteristics of this image type. In this paper, we investigate the visual redundancy modeling of composite images from an end-to-end multimodal perspective, including four cross-media modalities (i.e., text, brightness, color, and segmentation). Specifically, we design a two-stage cross-modal alignment module based on self-attention mechanism and contrastive learning, and develop a fusion module based on a cross-modal augmentation paradigm. Further, we establish the first cross-media visual redundancy dataset for composite images, which contains 413 groups of cross-modal data and generates 13629 realistic compression distortions using the latest versatile video coding (VVC) standard. Experimental results on nine benchmark datasets demonstrate the effectiveness of our method, outperforming seven representative methods.
Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their applicability. In this work we make the first attempt at Scene-Generalizable Interactive Segmentation in Radiance Fields (SGISRF) and propose a novel SGISRF method, which can perform 3D object segmentation for novel (unseen) scenes represented by radiance fields, guided by only a few interactive user clicks in a given set of multi-view 2D images. In particular, the proposed SGISRF focuses on addressing three crucial challenges with three specially designed techniques. First, we devise the Cross-Dimension Guidance Propagation to encode the scarce 2D user clicks into informative 3D guidance representations. Second, the Uncertainty-Eliminated 3D Segmentation module is designed to achieve efficient yet effective 3D segmentation. Third, Concealment-Revealed Supervised Learning scheme is proposed to reveal and correct the concealed 3D segmentation errors resulted from the supervision in 2D space with only 2D mask annotations. Extensive experiments on two real-world challenging benchmarks covering diverse scenes demonstrate 1) effectiveness and scene-generalizability of the proposed method, 2) favorable performance compared to classical method requiring scene-specific optimization.
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the source image. As a result, the process of extracting features from the source image is repeated in each interaction, resulting in substantial computational redundancy. In this work, we propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies and then recycles components that can be reused for each user interaction. Thus, the efficiency of the whole interactive process can be significantly improved. To be specific, we apply the Decoupling-Recycling strategy from three perspectives to address three types of discrepancies, respectively. First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately. Second, FDRN decouples high-level and low-level features from stratified semantic representations to enhance feature learning. Third, during the encoding of user guidance, current user guidance is decoupled from historical guidance to highlight the effect of current user guidance. We conduct extensive experiments on 6 datasets from different domains and modalities, which demonstrate the following merits of our model: 1) superior efficiency than other methods, particularly advantageous in the challenging scenarios requiring long-term interactions (up to 4.25x faster), while achieving favorable segmentation performance; 2) strong applicability to various methods serving as a universal enhancement technique; 3) well cross-task generalizability, e.g., to medical image segmentation, and robustness against misleading user guidance.
Video moment localization aims to retrieve the target segment of an untrimmed video according to the natural language query. Weakly supervised methods gains attention recently, as the precise temporal location of the target segment is not always available. However, one of the greatest challenges encountered by the weakly supervised method is implied in the mismatch between the video and language induced by the coarse temporal annotations. To refine the vision-language alignment, recent works contrast the cross-modality similarities driven by reconstructing masked queries between positive and negative video proposals. However, the reconstruction may be influenced by the latent spurious correlation between the unmasked and the masked parts, which distorts the restoring process and further degrades the efficacy of contrastive learning since the masked words are not completely reconstructed from the cross-modality knowledge. In this paper, we discover and mitigate this spurious correlation through a novel proposed counterfactual cross-modality reasoning method. Specifically, we first formulate query reconstruction as an aggregated causal effect of cross-modality and query knowledge. Then by introducing counterfactual cross-modality knowledge into this aggregation, the spurious impact of the unmasked part contributing to the reconstruction is explicitly modeled. Finally, by suppressing the unimodal effect of masked query, we can rectify the reconstructions of video proposals to perform reasonable contrastive learning. Extensive experimental evaluations demonstrate the effectiveness of our proposed method. The code is available at https://github.com/sLdZ0306/CCR https://github.com/sLdZ0306/CCR.
Most existing methods for image emotion classification extract features directly from images supervised by a single emotional label. However, this approach has a limitation known as the affective gap which restricts the capability of these features as they do not always align with the emotions perceived by users. To effectively bridge the affective gap, this paper proposes a visual content understanding network inspired by the human staged emotion perception process. The proposed network is comprised of three perception modules designed to extract multi-level information. Firstly, an entity perception module extracts entities from images. Secondly, an attribute perception module extracts the attribute content of each entity. Thirdly, an emotion perception module extracts emotion features based on both the entity and attribute information. We generate pseudo-labels of entities and attributes through image segmentation and vision-language models to provide auxiliary guidance for network learning. The progressive entity and attribute understanding enable the network to hierarchically extract semantic-level features for emotion analysis. Extensive experiments demonstrate that our progressive learning network achieves superior performance on various benchmark datasets for image emotion classification.
In this paper, we study the task of referring semantic segmentation in a highly practical setting, in which labeled visual data with corresponding text descriptions are available in the source, but only unlabeled visual data (without text descriptions) are available in the target. It is a challenging task that has many difficulties: (1) how to obtain proper queries for the target domain; (2) how to adapt visual-text joint distribution shifts; (3) how to maintain the original segmentation performance. Thus, we propose a cycle-consistent vision-language matching network to narrow down the domain gap and ease adaptation difficulty. Our model has significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. First, a pseudo-text selector is devised to handle the missing modality, through the pre-trained clip model to measure the gap between query features of the source and visual features of the target. Next, a cross-domain segmentation predictor is adopted, which prompts the joint representations to be domain invariant and minimize the discrepancy between two domains. Then, we present a cycle-consistent query matcher to learn discriminative features via reconstructing visual features from masks. Instead of doing the textual comparison, we match the visual features to the pseudo queries. Extensive experiments show the effectiveness of our method.
Currently, multimedia systems and computer vision algorithms are increasingly playing a crucial role in biological research. However, due to the significant difference between macro and micro scenarios, it is impractical to directly transfer existing computer vision methods to the images captured by microscopes. Taking social behavior analysis of worm for example, it heavily depends on accurate and efficient Multi-object tracking (MOT) methods. Meanwhile, it faces great challenges due to the unique physical characteristics of worm, such as small size, highly uniform appearance, rapid deformation and overlapping movement. This paper studies on the challenges and existing solutions for MOT in worm crowds by building a well-designed dataset ("WormTrack") and a tracking-by-detection benchmark. We observed that the state-of-the-art MOT methods suffers from considerable performance drop on the new dataset. Therefore, we propose a customized MOT method for worm crowds by deeply understanding the physical characteristics of worms and scenes. The method is composed by an instance segmentation based detector, a multiple model fused Kalman filter based tracker and a multi-constraint based trajectory repairer. The experimental results demonstrate that our method can accurately track over 100 worms with almost identical appearance for a long period, which is exceptional compared to existing methods. We hope our work will attract further researches to explore more in this new field, and promote the crossing field researches with biology and medicine. Our code and data is available at https://github.com/Jeerrzy/wormstudio.
Large-scale pre-trained vision-language models allow for the zero-shot text-based generation of 3D avatars. The previous state-of-the-art method utilized CLIP to supervise neural implicit models that reconstructed a human body mesh. However, this approach has two limitations. Firstly, the lack of avatar-specific models can cause facial distortion and unrealistic clothing in the generated avatars. Secondly, CLIP only provides optimization direction for the overall appearance, resulting in less impressive results. To address these limitations, we propose AvatarFusion, the first framework to use a latent diffusion model to provide pixel-level guidance for generating human-realistic avatars while simultaneously segmenting clothing from the avatar's body. AvatarFusion includes the first clothing-decoupled neural implicit avatar model that employs a novel Dual Volume Rendering strategy to render the decoupled skin and clothing sub-models in one space. We also introduce a novel optimization method, called Pixel-Semantics Difference-Sampling (PS-DS), which semantically separates the generation of body and clothes, and generates a variety of clothing styles. Moreover, we establish the first benchmark for zero-shot text-to-avatar generation. Our experimental results demonstrate that our framework outperforms previous approaches, with significant improvements observed in all metrics. Additionally, since our model is clothing-decoupled, we can exchange the clothes of avatars. Code are available on our project page https://hansenhuang0823.github.io/AvatarFusion.
Recently supervised contrastive learning (SCL) has achieved remarkable progress in semantic segmentation. Nevertheless, prior works have often necessitated a substantial number of samples to attain satisfactory performance, leading to a significant increase in training overhead. In this work, we leverage the idea of reweighting each pair to reduce the demand for large numbers of training samples in contrastive learning and propose a novel loss, dubbed confidence-aware contrastive (CAC) loss, which adaptively reweights each pair according to the predicted confidence for semantic segmentation. To alleviate the misalignment between supervised learning and contrastive learning, we further introduce an extra weight branch with a stop-gradient operator to generate the pair weights. Moreover, we present a confidence-aware marginal anchor sampling method for the calculation of supervised contrastive loss which focuses on marginal rather than the hardest pairs. Coupling with our method consistently improves the performance of various models (e.g. HRNet, OCRNet, SegFormer) on Cityscapes, ADE20K, PASCAL-Context, and COCO-Stuff datasets. Compared to existing SCL-based methods, the proposed method achieves competitive or even better results without relying on a memory bank or a large number of samples. Our code is at https://github.com/CVIU-CSU/Confidence-Aware-Contrastive-Loss.
Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model's ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets.
Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available.
Video action segmentation aims to densely cast each video frame into a set of pre-defined human action categories. This work proposes a novel model, dubbed as diffused Fourier network (DFN) for video action segmentation. It advances the research frontier by addressing several central bottlenecks in the existing methods for video action segmentation. First, capturing long-range dependence among video frames is known to be crucial for precisely estimating the temporal boundaries for actions. Rather than relying on compute-intensive self-attention modules or stacking multi-rate dilated convolutions as in previous models (e.g., ASFormer), we devise Fourier token mixer over shiftable temporal windows in the video sequence, which harnesses the parameter-free and light-weighted Fast Fourier Transform (FFT) for efficient spectral-temporal feature learning. Essentially, even simple spectral operations (e.g., pointwise product) bring global receptive field across the entire temporal window. The proposed Fourier token mixer thus provides a low-cost alternative for existing practice. Secondly, the results of action segmentation tend to be fragmented, primarily due to the noisy per-frame action likelihood, known as over-segmentation in the literature. Inspired by the recently-proposed diffusion models, we treat over-segments as noises corrupting the true temporal boundaries, and conduct denoising via a recurrent execution of a parameter-sharing module, akin to the backward denoising process in the diffusion models. Comprehensive experiments on three video benchmarks (GTEA, 50salads and Breakfast) have clearly validated that the proposed method can strike an excellent balance between computations / parameter count and accuracy.
Consistency regularization has been widely studied in recent semi- supervised semantic segmentation methods, and promising per- formance has been achieved. In this work, we propose a new con- sistency regularization framework, termed mutual knowledge dis- tillation (MKD), combined with data and feature augmentation. We introduce two auxiliary mean-teacher models based on consis- tency regularization. More specifically, we use the pseudo-labels generated by a mean teacher to supervise the student network to achieve a mutual knowledge distillation between the two branches. In addition to using image-level strong and weak augmentation, we also discuss feature augmentation. This involves considering various sources of knowledge to distill the student network. Thus, we can significantly increase the diversity of the training samples. Experiments on public benchmarks show that our framework out- performs previous state-of-the-art (SOTA) methods under various semi-supervised settings. Code is available at https://github.com/jianlong-yuan/semi-mmseg.
We investigate transductive zero-shot point cloud semantic segmentation, where the network is trained on seen objects and able to segment unseen objects. The 3D geometric elements are essential cues to imply a novel 3D object type. However, previous methods neglect the fine-grained relationship between the language and the 3D geometric elements. To this end, we propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives. Therefore, guided by language, the network recognizes the novel objects represented with geometric primitives. Specifically, we formulate a novel point visual representation, the similarity vector of the point's feature to the learnable prototypes, where the prototypes automatically encode geometric primitives via back-propagation. Besides, we propose a novel Unknown-aware InfoNCE Loss to fine-grained align the visual representation with language. Extensive experiments show that our method significantly outperforms other state-of-the-art methods in the harmonic mean-intersection-over-union (hIoU), with the improvement of 17.8%, 30.4%, 9.2% and 7.9% on S3DIS, ScanNet, SemanticKITTI and nuScenes datasets, respectively. Codes are available1 https://github.com/runnanchen/Zero-Shot-Point-Cloud-Segmentation.
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again. However, it is not economical to train diverse teacher models for the disposable distillation. In this paper, we introduce a new concept dubbed Avatars for distillation, which are the inference ensemble models derived from the teacher. Concretely, (1) For each iteration of distillation training, various Avatars are generated by a perturbation transformation. We validate that Avatars own higher upper limit of working capacity and teaching ability, aiding the student model in learning diverse and receptive knowledge perspectives from the teacher model. (2) During the distillation, we propose an uncertainty-aware factor from the variance of statistical differences between the vanilla teacher and Avatars, to adjust Avatars' contribution on knowledge transfer adaptively. Avatar Knowledge Distillation (AKD) is fundamentally different from existing methods and refines with the innovative view of unequal training. Comprehensive experiments demonstrate the effectiveness of our Avatars mechanism, which polishes up the state-of-the-art distillation methods for dense prediction without more extra computational cost. The AKD brings at most 0.7 AP gains on COCO 2017 for Object Detection and 1.83 mIoU gains on Cityscapes for Semantic Segmentation, respectively.