Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack the precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. With our dataset, we were able to achieve an increase of 10.74% in the Recall rate, and a decrease of 33.10% in the False Positive Rate (FPR) from the industrial simulation experiment. Furthermore, we introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images, even when working with limited defective data. The synthetic images generated by Defect-Gen significantly enhance the performance of defect segmentation models, achieving an improvement in mIoU scores up to 9.85 on Defect-Spectrum subsets. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering a solid platform for testing and refining advanced models. Our codes and datasets are released in https://envision-research.github.io/Defect_Spectrum.
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Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and point clouds on a unified transformer model. This approach integrates the modality-specific properties of images and point clouds and achieves fundamental downstream tasks in image and 3D object recognition on a unified transformer model by learning visual-geometric representations. In this work, we introduce Formula-Supervised Visual-Geometric Pre-training (FSVGP), a novel synthetic pre-training method that automatically generates aligned synthetic images and point clouds from mathematical formulas. Through cross-modality supervision, we enable supervised pre-training between visual and geometric modalities. FSVGP also reduces reliance on real data collection, cross-modality alignment, and human annotation. Our experimental results show that FSVGP pre-trains more effectively than VisualAtom and PC-FractalDB across six tasks: image and 3D object classification, detection, and segmentation. These achievements demonstrate FSVGP's superior generalization in image and 3D object recognition and underscore the potential of synthetic pre-training in visual-geometric representation learning.
Spiking Neural Networks (SNNs) offer a biologically inspired approach to computer vision that can lead to more efficient processing of visual data with reduced energy consumption. However, maintaining homeostasis within SNNs is challenging, as it requires continuous adjustment of neural responses to preserve equilibrium and optimal processing efficiency amidst diverse and often unpredictable input signals. In response to these challenges, we propose the Asynchronous Bioplausible Neuron (ABN), a dynamic spike firing mechanism that offers a simple yet potent auto-adjustment to variations in input signals. Its parameters, Membrane Gradient (MG), Threshold Retrospective Gradient (TRG), and Spike Efficiency (SE), make it stand out for its easy implementation, significant effectiveness, and proven reduction in power consumption, a key innovation demonstrated in our experiments. Comprehensive evaluation across various datasets demonstrates ABN's enhanced performance in image classification and segmentation, maintenance of neural equilibrium, and energy efficiency. The code will be publicly available on the GitHub Project Page.
Rethinking and Improving Visual Prompt Selection for In-Context Learning Segmentation Framework
PDF ↗As a fundamental and extensively studied task in computer vision, image segmentation aims to locate and identify different semantic concepts at the pixel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting specific objects. However, existing works mostly ignore the value of visual prompts or simply apply similarity sorting to select contextual examples. In this paper, we focus on rethinking and improving the example selection strategy. By comprehensive comparisons, we first demonstrate that ICL-based segmentation models are sensitive to different contexts. Furthermore, empirical evidence indicates that the diversity of contextual prompts plays a crucial role in guiding segmentation. Based on the above insights, we propose a new stepwise context search method. Different from previous works, we construct a small yet rich candidate pool and adaptively search the well-matched contexts. More importantly, this method effectively reduces the annotation cost by compacting the search space. Extensive experiments show that our method is an effective strategy for selecting examples and enhancing segmentation performance.
We introduce PartSTAD, a method designed for the task adaptation of 2D-to-3D segmentation lifting. Recent studies have highlighted the advantages of utilizing 2D segmentation models to achieve high-quality 3D segmentation through few-shot adaptation. However, previous approaches have focused on adapting 2D segmentation models for domain shift to rendered images and synthetic text descriptions, rather than optimizing the model specifically for 3D segmentation. Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation. We introduce weights for 2D bounding boxes for adaptive merging and learn the weights using a small additional neural network. Additionally, we incorporate SAM, a foreground segmentation model on a bounding box, to improve the boundaries of 2D segments and consequently those of 3D segmentation. Our experiments on the PartNet-Mobility dataset show significant improvements with our task adaptation approach, achieving a 7.0%p increase in mIoU and a 5.2%p improvement in mAP@50 for semantic and instance segmentation compared to the SotA few-shot 3D segmentation model. The code will be released publicly later.
Current visual foundation models are trained purely on unstructured 2D data, limiting their understanding of 3D structure of objects and scenes. In this work, we show that fine-tuning on 3D-aware data improves the quality of emerging semantic features. We design a method to lift semantic 2D features into an efficient 3D Gaussian representation, which allows us to re-render them for arbitrary views. Using the rendered 3D-aware features, we design a fine-tuning strategy to transfer such 3D knowledge into a 2D foundation model. We demonstrate that models fine-tuned in that way produce features that readily improve downstream task performance in semantic segmentation and depth estimation through simple linear probing. Notably, though fined-tuned on a single indoor dataset, the improvement is transferable to a variety of indoor datasets and out-of-domain datasets. We hope our study encourages the community to consider injecting 3D knowledge when training 2D foundation models. Code will be released upon acceptance of the paper.
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
PDF ↗Human-centric perception (\eg detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision. This paper introduces a unified and versatile framework (HQNet) for single-stage multi-person multi-task human-centric perception (HCP). Our approach centers on learning a unified human query representation, denoted as Human Query, which captures intricate instance-level features for individual persons and disentangles complex multi-person scenarios. Although different HCP tasks have been well-studied individually, single-stage multi-task learning of HCP tasks has not been fully exploited in the literature due to the absence of a comprehensive benchmark dataset. To address this gap, we propose COCO-UniHuman benchmark to enable model development and comprehensive evaluation. Experimental results demonstrate the proposed method's state-of-the-art performance among multi-task HCP models and its competitive performance compared to task-specific HCP models. Moreover, our experiments underscore Human Query's adaptability to new HCP tasks, thus demonstrating its robust generalization capability. Codes and data are available at \url{https://github.com/lishuhuai527/COCO-UniHuman}.
Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose HIMO, a large-scale MoCap dataset of full-body human interacting with multiple objects, containing 3.3K 4D HOI sequences and 4.08M 3D HOI frames. We also annotate HIMO with detailed textual descriptions and temporal segments, benchmarking two novel tasks of HOI synthesis conditioned on either the whole text prompt or the segmented text prompts as fine-grained timeline control. To address these novel tasks, we propose a dual-branch conditional diffusion model with a mutual interaction module for HOI synthesis. Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions. Our data, codes, and models will be publicly available for research purposes.
The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples, rather than considering a more effective sampling from a distribution of positive and negative embeddings. This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2). IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Furthermore, our contrastive learning employs informative samples drawn from a distribution of positive and negative embeddings learned from the entire training set. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field. Code will be available.
Micro-expressions (MEs) are involuntary and quickly displayed facial expressions that reveal subtle psychological activities. Most previous research typically focused on two separate tasks: micro-expression spotting and recognition. We aim to propose a high-precision "spotting+recognition" method that can spot ME intervals from long videos and recognize their emotional categories. Due to the occurrence sparsity of MEs, there is a significant imbalance between the number of micro-expression intervals and non-micro-expression intervals in long videos. This imbalance makes it challenging for models trained using conventional strategies to distinguish true MEs from noise samples caused by head movements, blinking, and macro-expressions, resulting in a high false-positive-rate and reducing the overall performance. We reduce the number of smooth segments to alter the data distribution within the non-micro-expression (non-ME) category. This adjustment enables the model to focus more on the subtle differences between noise samples and ME samples. To achieve this, we design an ingenious training data preparation strategy: using false positive samples from the initial spotting results as non-ME category samples, and using true positive and false negative samples from the initial spotting as emotion category samples. These are combined as the training data, creating a recognition model capable of both emotion classification and non-ME category determination. Additionally, we propose a three-stage micro-expression analysis method, including ME spotting, ME recognition and non-ME intervals removal module. Our method is validated through five-fold cross-validation experiments on the CAS(ME)² and SAMM Long Video datasets, achieving a overall STRS metric of 0.16, which significantly outperformed baseline methods and demonstrated the effectiveness of our approach.
Recently, automatic micro-expression (ME) analysis has attracted increasing attention, since ME is a spontaneous facial expression that can truly reflect the emotional state an individual tries to conceal. As a crucial step in ME analysis, Micro- and Macro-expression (MaE) spotting aims to sequentially identify the occurrence intervals of MEs and MaEs within a long video sequence. However, the subtle spatiotemporal movements of MEs and the scarcity of well-labeled data pose great challenges for accurately spotting them. To this end, this paper proposes a novel spotting framework based on Multi-scale Feature Learning Network with Optical Flow Correction. Specifically, we first integrate the pre-trained VideoMAE and customized convolutional layers as a visual feature extraction module to learn the facial motion features in long video sequences. Then, to comprehensively locate and identify the existing ME and MaE segments, we introduce a multi-scale candidate segment generation method based on the ActionFormer. In particular, a multi-start points optical flow filtering method is proposed to improve the precision of expression spotting. Finally, we conduct comprehensive experiments on the MEGC2024 spotting task, and the experimental results demonstrate the effectiveness of our method, which ranks second in this task. The implemented code is also publicly available at https://github.com/zzy188zzy/megc_spotting_code.
Micro-expressions, as a type of facial expression corresponding to macro-expressions, usually have a short duration and low intensity. Due to these characteristics, micro-expression spotting holds significant value in medical care and public safety. Recent years have witnessed advancements in micro-expression spotting methods; however, spotting micro-expressions remains a challenging task due to their brief duration and low intensity. In this paper, we propose a micro-expression spotting method based on optical flow features with boundary calibration. We first perform face detection, cropping, and alignment on images containing faces. Then, regions of interest (ROIs) are defined, and optical flow features are extracted. Furthermore, candidate expression segments are identified based on the magnitude of the processed optical flows. Finally, a boundary calibration module is utilized to calibrate the boundaries. The effectiveness of the proposed method is evaluated on the MEGC2024 test set, resulting in an overall F1-score of 0.27.
Recent years have seen a revolution in the creation of synthetic multimedia content. Algorithms with the ability to generate truly convincing images, videos, text and audio capable of fooling any human being. In addition to the possible beneficial uses that this type of technology may have, we must highlight the danger of its misuse for criminal or fraudulent activities. Deepfakes stand out as an example of a potentially dangerous use of these technologies, since they facilitate identity theft and the generation of misinformation. Current solutions are not capable of detecting this type of fake content with sufficient reliability. Therefore, it is crucial to develop new algorithms that solve this problem. This paper presents two methods focusing on the classification and localization of deepfake videos taking into account audio and visual information. These methods were submitted to the ACM 1M Deepfakes Detection Challenge, achieving the highest score in the temporal localization task and a top-five ranking in the classification task.
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M.
In numerous medical scenarios, segmenting clinical targets is highly subjective, influenced by the doctors' expertise and preferences, which results in significant multi-rater variability. This inherent annotation ambiguity poses a challenge for the practical deployment of data-driven techniques and raises concerns about the reliability of automatic predictions by medical artificial intelligence (AI) systems. To address this issue, we host a grand challenge (MMIS-2024) at ACM MM '24 to explore the problem of multi-rater medical image segmentation. First, we have released two datasets publicly, one on nasopharyngeal carcinoma (NPC) and the other on glioblastoma (GBM). For NPC, one challenge track encourages participants to develop models that utilize the four expert-provided labels per sample. The second GBM track explores the one-sample-one-label setting in the context of multi-rater segmentation. Here, different experts annotated different GBM samples for training. Finally, to assess the submissions, we employ two distinct sets of metrics, designed to evaluate prediction diversity and personalization, respectively. By exploring the two tasks with different metrics, the MMIS-2024 challenge aims to establish a global benchmark for multi-rater medical image segmentation, facilitating clinical AI deployments.
Point clouds have the strong capability for modeling 3D objects and scenes, which can be widely used in diverse applications and thus generate the burdens of transmission and storage. Efficient compression algorithms have been explored extensively, and research efforts have also been invested to enhancement algorithms. Moreover, the quality of point clouds can influence 3D analysis tasks, e.g., classification, segmentation, detection, and multimodal understanding, etc. Recent 3D multimodal large models can bring better perception optimizations. This tutorial will provide the fundamental knowledge for point cloud compression, enhancement and applications, and place emphasis on the influences of point cloud quality to human and machine perceptions. We will also discuss the progress of international standards and open source projects for point cloud technologies. From this tutorial, audiences are expected to grasp the basic knowledge and recent progress of point cloud technologies, and promote the research developments in both academia and industrial communities.
We present Sophia-in-Audition (SiA), a new frontier in virtual production, by employing the humanoid robot Sophia within an UltraStage environment composed of a controllable lighting dome coupled with multiple cameras. We demonstrate Sophia's capability to replicate iconic film segments, follow real performers, and perform a variety of motions and expressions, showcasing her versatility as a virtual actor. Key to this process is the integration of facial motion transfer algorithms and the UltraStage's controllable lighting and multi-camera setup, enabling dynamic performances that align with the director's vision. Our comprehensive user studies indicate positive audience reception towards Sophia's performances, highlighting her potential to reduce the uncanny valley effect in virtual acting. Additionally, the immersive lighting in dynamic clips was highly rated for its naturalness and its ability to mirror professional film standards. The paper presents a first-of-its-kind multi-view robot performance video dataset with dynamic lighting, offering valuable insights for future enhancements in humanoid robotic performers and virtual production techniques. This research contributes significantly to the field by presenting a unique virtual production setup, developing tools for sophisticated performance control, and providing a comprehensive dataset and user study analysis for diverse applications.
This paper presents a novel model protection paradigm Model Locking that locks the performance of a finetuned model on private data to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the private finetuning data into unique styles or blend new objects into the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the same text prompt used to edit the data. We conduct extensive experiments on both image classification and segmentation tasks and show that 1) ModelLock can effectively lock finetuned models without significantly reducing their unlocked performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models.
Maps are the fundamental elements of any navigation and localization system. With the fast expansion of urban areas and the increasing complexity of modern cities, traditional mapping techniques cannot meet the need for frequent map updates with enriched map details. This work investigates how geo-referenced very high-resolution (VHR) RGB satellite imagery can be used to extract geo-information to support the creation of fine-scale maps and facilitate map updates, focusing on two sub-directions. First, to alleviate the data scarcity issue of large-scale geo-information extraction from satellite imagery-based datasets, (1) GAN-assisted road segmentation proposes a new assisted training scheme to improve the model performance when the training dataset is limited and (2) a context-enhanced satellite-imagery dataset is created for large-scale parking lot detection to improve the type diversity of target objects. Second, to support rich map attribute geo-information extraction and vision-based navigation using street-view imagery, new methods are proposed to improve location and orientation extraction of the street-view imagery via cross-view matching with satellite imagery.
Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained neural codec for various contents. Though, its application in neural video compression (NVC) is still limited due to two main aspects: 1), video compression relies heavily on temporal redundancy, therefore updating just one or a few frames can lead to significant errors accumulating over time; 2), NVC frameworks are generally complex, with many comprehensive components that are not trivial to update quickly during the encoding procedure. To address these challenges, we have developed a content-adaptive NVC technique called Group-aware Parameter-efficient Updating (GPU). Initially, to minimize error accumulation, we adopt a group-aware approach for updating encoder parameters. This involves adopting a patch-based Group of Pictures (GoP) updating strategy to segment a video into patch-based GoPs, which will be updated to facilitate a globally optimized domain-transferable solution. Subsequently, we introduce a parameter-efficient delta-tuning strategy, which is achieved by integrating several light-weight adapters into each encoding component by using both serial and parallel configuration. Such architecture-agnostic modules stimulate the components with large parameters, thereby reducing the updating cost during the encoding stage. We incorporate our GPU into the latest NVC framework and conduct extensive experiments, whose results showcase outstanding video compression efficiency across six video compression benchmarks and the adaptability of one medical volumetric image compression benchmark.