Object pose tracking is one of the pivotal technologies in multimedia, attracting ever-growing attention in recent years. Existing methods employing traditional cameras encounter numerous challenges such as motion blur, sensor noise, partial occlusion, and changing lighting conditions. The emerging bio-inspired sensors, particularly event cameras, possess advantages such as high dynamic range and low latency, which hold the potential to address the aforementioned challenges. In this work, we present an optical flow-guided 6DoF object pose tracking method with an event camera. A 2D-3D hybrid feature extraction strategy is firstly utilized to detect corners and edges from events and object models, which characterizes object motion precisely. Then, we search for the optical flow of corners by maximizing the event-associated probability within a spatio-temporal window, and establish the correlation between corners and edges guided by optical flow. Furthermore, by minimizing the distances between corners and edges, the 6DoF object pose is iteratively optimized to achieve continuous pose tracking. Experimental results of both simulated and real events demonstrate that our methods outperform event-based state-of-the-art methods in terms of both accuracy and robustness.
论文检索
输入标题、作者或关键词，从 1,237 篇学术成果中精准定位
Short videos turn into an important channel for public sharing, as well as they've become a fertile ground for fake news. Fake news video detection is to judge the veracity of news based on its different modal information, such as video, audio, text, image and social context information. Current detection models tend to learn the multimodal dataset biases within spurious correlations between news modalities and veracity labels as shortcuts, rather than learning how to integrate the multimodal information behind them to reason, resulting in seriously degrading their detection and generalization capabilities. To address this issues, we propose a Multimodal Multi-View Debiasing (MMVD) framework, which makes the first attempt to mitigate various multimodal biases for fake news video detection. Inspired by people's misleading situations by multimodal short videos, we summarize three cognitive biases: static, dynamic and social biases. MMVD put forward a multi-view causal reasoning strategy to learn unbiased dependencies within the cognitive biases, thus enhancing the unbiased prediction of multimodal videos. The extensive experimental results show that the MMVD could improve the detection performance of multimodal fake news video. Studies also confirm that our MMVD can mitigate multiple biases on complex real-world scenarios and improve generalization ability of fake news video detection.
Due to the inherent vulnerability of neural networks, adversarial attacks present formidable challenges to the robustness and reliability of deep learning models. In contrast to traditional adversarial training (AT) methods that prioritize semantic distillation and purification, our work pioneers a novel discovery attributing the insufficient adversarial robustness of models to the challenges of spatial attention shift and channel activation disarray. To mitigate these issues, we propose a robust spatial-aligned and channel-adapted learning paradigm, which we term the "StayFocused", that integrates spatial alignment and channel adaptation to enhance the focus region against adversarial attacks by adaptively recalibrating the spatial attention and channel responses. Specifically, the proposed StayFocused mainly benefits from two flexible mechanisms, i.e., Spatial-aligned Hypersphere Constraint (SHC) and Channel-adapted Prompting Calibration (CPC). Specifically, SHC aims to enhance intra-class compactness and inter-class separation between adversarial and natural samples by measuring the angular margins and distribution distance within the hypersphere space. Inspired by the top-K candidate prompts from the clean sample, CPC is designed to dynamically recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels. To comprehensively learn feature representations, the StayFocused framework can be easily extended with additional branches in a multi-head training manner, further enhancing the model's robustness and adaptability. Extensive experiments on multiple benchmark datasets consistently demonstrate the effectiveness and superiority of our StayFocused over state-of-the-art baselines.
Temporal Sentence Grounding (TSG), which aims to localize events in untrimmed videos with a given language query, has been widely studied in the last decades. However, recently researchers have demonstrated that previous approaches are severely limited in out-of-distribution generalization, thus proposing the De-biased TSG challenge which requires models to overcome weakness towards outlier test samples. In this paper, we design a novel framework, termed Counterfactually-Augmented Event Matching (CAEM), which incorporates counterfactual data augmentation to learn event-query joint representations to resist the training bias. Specifically, it consists of three components: (1) A Temporal Counterfactual Augmentation module that generates counterfactual video-text pairs by temporally delaying events in the untrimmed video, enhancing the model's capacity for counterfactual thinking. (2) An Event-Query Matching model that is used to learn joint representations and predict corresponding matching scores for each event candidate. (3) A Counterfact-Adaptive Framework (CAF) that incorporates the counterfactual consistency rules on the matching process of the same event-query pairs, furtherly mitigating the bias learned from training sets. We conduct thorough experiments on two widely used DTSG datasets, i.e., Charades-CD and ActivityNet-CD, to evaluate our proposed CAEM method. Extensive experimental results show our proposed CAEM method outperforms recent state-of-the-art methods on all datasets. Our implementation code is available at https://github.com/CFM-MSG/CAEM_Code.
Prompt-Guided Image-Adaptive Neural Implicit Lookup Tables for Interpretable Image Enhancement
PDF ↗In this paper, we delve into the concept of interpretable image enhancement, a technique that enhances image quality by adjusting filter parameters with easily understandable names such as "Exposure'' and "Contrast''. Unlike using predefined image editing filters, our framework utilizes learnable filters that acquire interpretable names through training. Our contribution is two-fold. Firstly, we introduce a novel filter architecture called an image-adaptive neural implicit lookup table, which uses a multilayer perceptron to implicitly define the transformation from input feature space to output color space. By incorporating image-adaptive parameters directly into the input features, we achieve highly expressive filters. Secondly, we introduce a prompt guidance loss to assign interpretable names to each filter. We evaluate visual impressions of enhancement results, such as exposure and contrast, using a vision and language model along with guiding prompts. We define a constraint to ensure that each filter affects only the targeted visual impression without influencing other attributes, which allows us to obtain the desired filter effects. Experimental results show that our method outperforms existing predefined filter-based methods, thanks to the filters optimized to predict target results. Our source code is available at https://github.com/satoshi-kosugi/PG-IA-NILUT.
3D textured face reconstruction from sketches applicable in many scenarios such as animation, 3D avatars, artistic design, missing people search, etc., is a highly promising but underdeveloped research topic. On the one hand, the stylistic diversity of sketches leads to existing sketch-to-3D-face methods only being able to handle pose-limited and realistically shaded sketches. On the other hand, texture plays a vital role in representing facial appearance, yet sketches lack this information, necessitating additional texture control in the reconstruction process. This paper proposes a novel method for reconstructing controllable textured and detailed 3D faces from sketches, named S2TD-Face. S2TD-Face introduces a two-stage geometry reconstruction framework that directly reconstructs detailed geometry from the input sketch. To keep geometry consistent with the delicate strokes of the sketch, we propose a novel sketch-to-geometry loss that ensures the reconstruction accurately fits the input features like dimples and wrinkles. Our training strategies do not rely on hard-to-obtain 3D face scanning data or labor-intensive hand-drawn sketches. Furthermore, S2TD-Face introduces a texture control module utilizing text prompts to select the most suitable textures from a library and seamlessly integrate them into the geometry, resulting in a 3D detailed face with controllable texture. S2TD-Face surpasses existing state-of-the-art methods in extensive quantitative and qualitative experiments. Our project is available at https://github.com/wang-zidu/S2TD-Face.
Visual prompting is an efficient methodology for finetuning pretrained visual models by introducing a small number of learnable parameters while keeping the backbone frozen. However, most existing visual prompting methods learn a shared prompt for all samples, making it challenging to grasp distinct characteristics among diverse samples, thereby limiting the model's performance. While other methods partially address this issue through sample clustering and learning multiple prompts, they still struggle to capture nuanced differences among instances and incur significant parameter overhead. Therefore, to comprehensively and efficiently leverage discriminative characteristics of individual instances, we propose an Instance Visual Prompting method, called InsVP. Initially, the instance image prompt is introduced to extract both crucial and nuanced discriminative information from the original image itself and is overlaid onto the input image. Furthermore, the instance feature prompt is designed to capture both commonalities and characteristics among individual instances, fed into the model's intermediate layers to facilitate feature extraction. Consequently, the instance image and feature prompts complement each other, enhancing the adaptation ability of pretrained models to extract discriminative features from individual instances. Extensive experiments on various large-scale benchmarks show that our InsVP achieves superior performance exceeding the state-of-the-art methods at a lower parameter cost. The code is available at https://github.com/zhoujiahuan1991/MM2024-InsVP .
Sentiment analysis and complaint identification are key tools in mining user preferences by measuring the polarity and breach of expectations. Recent works on complaint identification identify aspect categories and classify them into complaint or non-complaint classes. However, aspect category-based complaint identification provides high-level information about the features of products. In addition, it is also observed that the user sometimes does not complain about a specific aspect but expresses concern about specific aspects in a respectful way. Currently, unimodal and multimodal studies do not differentiate this thin line between complaint and concern. In this work, we propose the task of multimodal aspect term-based analysis beyond sentiments and complaints. It comprises of two sub-tasks, viz (i) classification of the given aspect term into one of the four classes, viz. praise, concern, complaint, and others, (ii) identification of the cause of praise, concern, and complaint classes. We propose a first benchmark explainable multimodal corpus annotated for aspect term-based complaints, praises, concerns, their corresponding causes, and sentiments. Further, we propose an effective technique for the joint learning of aspect term-based complaint/concern/praise identification and cause extraction tasks (primary tasks) where sentiment analysis is used as a secondary task to assist primary tasks and establish them as baselines for further research in this direction. The dataset has been made available on https://www.iitp.ac.in/~ai-nlp-ml/resources.html and at Github repository: https://github.com/20118/MAspectX.
While neural radiance fields (NeRF) have shown promise in novel view synthesis, their implicit representation limits explicit control over object manipulation. Existing research has proposed the integration of explicit geometric proxies to enable deformation. However, these methods face two primary challenges: firstly, the time-consuming and computationally demanding tetrahedralization process; and secondly, handling complex or thin structures often leads to either excessive, storage-intensive tetrahedral meshes or poor-quality ones that impair deformation capabilities. To address these challenges, we propose DeformRF, a method that seamlessly integrates the manipulability of tetrahedral meshes with the high-quality rendering capabilities of feature grid representations. To avoid ill-shaped tetrahedra and tetrahedralization for each object, we propose a two-stage training strategy. Starting with an almost-regular tetrahedral grid, our model initially retains key tetrahedra surrounding the object and subsequently refines object details using finer-granularity mesh in the second stage. We also present the concept of recursively subdivided tetrahedra to create higher-resolution meshes implicitly. This enables multi-resolution encoding while only necessitating the storage of the coarse tetrahedral mesh generated in the first training stage. We conduct a comprehensive evaluation of our DeformRF on both synthetic and real-captured datasets. Both quantitative and qualitative results demonstrate the effectiveness of our method for novel view synthesis and deformation tasks. Project page: https://ustc3dv.github.io/DeformRF/
We present MegaSurf, a Neural Surface Reconstruction (NSR) framework to reconstruct 3D models of large scenes from aerial images. Many methods utilize geometry cues to overcome the shape-radiance ambiguity, which would produce large geometric errors. In addition, directly using inevitable imprecise geometric cues would lead to degradation in the reconstruction results, especially on large-scale scenes. To address this phenomenon, we propose a Learnable Geometric Guider (LG Guider) to learn a sampling field from reliable geometric cues. The LG Guider decides which position should fit the input radiance and can be continuously refined by rendering loss. Our MegaSurf uses a Divide-and-Conquer training strategy to address the synchronization issue between the Guider and the lagging NSR's radiance field. This strategy enables the Guider to transmit the information it carried to the radiance field without being disrupted by the gradients back-propagated from the lagging rendering loss at the early stage of training. Furthermore, we propose a Fast PatchMatch MVS module to derive the geometric cues in the planer regions that help overcome ambiguity. Experiments on several aerial datasets show that MegaSurf can overcome ambiguity while preserving high-fidelity details. Compared to SOTA methods, MegaSurf achieves superior reconstruction accuracy of large scenes and boosts the acquisition of geometric cues more than four times.
Image-to-image (i2i) translation has achieved notable success, yet remains challenging in scenarios like real-to-illustrative style transfer of fashion. Existing methods focus on enhancing the generative model with diversity while lacking ID-preserved domain translation. This paper introduces a novel model named Uni-DlLoRA to release this constraint. The proposed model combines the original images within a pretrained diffusion-based model using the proposed Uni-adapter extractors, while adopting the proposed Dual-LoRA module to provide distinct style guidance. This approach optimizes generative capabilities and reduces the number of additional parameters required. In addition, a new multimodal dataset featuring higher-quality images with captions built upon an existing real-to-illustration dataset is proposed. Experimentation validates the effectiveness of our proposed method.
Landscapes, recognized for their indispensable role in large-scale scenes, are experiencing growing demand. However, the manual modeling of such content is labor-intensive and lacks efficiency. Procedural Content Generation (PCG) techniques enable the rapid generation of diverse landscape elements. Nevertheless, ordinary users may encounter difficulties controlling these methods for desired results. In this paper, we introduce a controllable framework for procedurally generating landscapes. We integrate state-of-the-art Large Language Models (LLMs) to enhance user accessibility and control. By converting plain text inputs into parameters through LLMs, our framework allows ordinary users to generate a batch of plausible landscapes tailored to their specifications. A parameter-controlled PCG procedure is designed to leverage optimization techniques and employ rule-based refinements. It achieves harmonious layering in terrains, zoning, and roads while enabling aesthetic arrangement of vegetation and artificial elements. Extensive experiments demonstrate our framework's effectiveness in generating landscapes comparable to those crafted by experienced architects. Our framework has the potential to enhance the productivity of landscape designers significantly.
Point cloud plays a significant role in recent learning-based vision tasks, which contain additional information about the physical space compared to 2D images. However, such a 3D data format also results in more expensive training costs to train a sophisticated network with large 3D datasets. Previous methods for point cloud compression focus on compacting the representation of each point cloud for better storage and transmission but ignore the improvements in training efficiency. In this paper, we introduce a new open problem in the point cloud field, named point cloud condensation : Can we condense a large point cloud dataset into a much smaller synthetic dataset while preserving the important information of the original large dataset? In other words, we explore the possibility of training a network on a smaller dataset of informative point clouds extracted from the original large dataset but maintaining similar network classification performance. Training on this small synthetic dataset could largely improve the training efficiency. To achieve this goal, we propose a two-stage approach to generate the synthetic dataset. We first introduce a nearest-feature-mean based strategy to initialize the synthetic dataset, and then formulate our goal as a parameter-matching problem, which we solve by introducing a gradient-matching strategy to iteratively refine the synthetic dataset. We conduct extensive experiments on various synthetic and real-scanned 3D object classification benchmarks, showing that our synthetic dataset could achieve almost the same performance with only 5% point clouds of ScanObjectNN dataset compared to training with the full dataset. Codes are available at https://github.com/XLechter/PointCondensation.
The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an Uncertainty-based degradation representation for blind Super-Resolution framework (USR). By suppressing the uncertainties of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. The VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach.
Video frame interpolation is a critical component of video streaming, a vibrant research area dealing with requests of both service providers and users. However, existing methods cannot handle changing video resolutions while improving user perceptual quality. We aim to unleash the multifaceted knowledge yielded by the hierarchical views at multiple scales in a pyramid network. Specifically, we build a dual-view pyramid network by introducing pyramidal dual-view correspondence matching. It compels each scale to actively seek knowledge in view of both the current scale and a coarser scale, conducting robust correspondence matching by considering neighboring scales. Meanwhile, an auxiliary multi-scale collaborative supervision is devised to enforce the exchange of knowledge among scales and thus reduce error propagation from coarse to fine scales. Based on the robust capture of video dynamics via pyramidal dual-view correspondence matching, we further construct a pyramidal refinement module that formulates frame refinement as progressive latent representation generations by developing flow-guided cross-scale attention for feature fusion among frames. The proposed method is able to improve the perceptual quality on several benchmarks of varying video resolutions, while keeping low distortion and a compact model size.
Elliptical Object Detection (EOD) is crucial yet challenging due to complex scenes and varying object characteristics. Existing methods often struggle with parameter configurations and lack adaptability in label-scarce scenarios. To address this, we propose a new semi-supervised teacher-student framework, namely Dual-Teacher Collaborative Guidance (DTCG), comprising a five-parameter teacher detector, a six-parameter teacher detector, and a student detector. DTCG allows the two teachers specializing in different regression approaches, and co-instructing the student within a unified model, so as to prevent errors and enhance performance. Additionally, a feature correlation module highlights differences between teacher features and employs deformable convolution to select advantageous features for final parameter regression. Furthermore, we devise a collaborative training strategy to update the two teachers asynchronously. Extensive experiments conducted on two widely recognized datasets affirm the superior performance of our DTCG over other leading competitors across various semi-supervised scenarios. Notably, our method achieves a 5.61% higher performance than the second best method when utilizing only 10% annotated data. Code is available at https://github.com/FengLongHan/DTCG.
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network. Despite demonstrated success, such networks suffer from some fundamental problems: 1) It contains pooling operation to cope with inherently disordered point clouds, hindering the capture of 3D spatial information that is useful for tracking, a regression task. 2) The adopted set abstraction operation hardly handles density-inconsistent point clouds, also preventing 3D spatial information from being modeled. To solve these problems, we introduce a novel tracking framework, termed VoxelTrack. By voxelizing inherently disordered point clouds into 3D voxels and extracting their features via sparse convolution blocks, VoxelTrack effectively models precise and robust 3D spatial information, thereby guiding accurate position prediction for tracked objects. Moreover, VoxelTrack incorporates a dual-stream encoder with cross-iterative feature fusion module to further explore fine-grained 3D spatial information for tracking. Benefiting from accurate 3D spatial information being modeled, our VoxelTrack simplifies tracking pipeline with a single regression loss. Extensive experiments are conducted on three widely-adopted datasets including KITTI, NuScenes and Waymo Open Dataset. The experimental results confirm that VoxelTrack achieves state-of-the-art performance (88.3%, 71.4% and 63.6% mean precision on the three datasets, respectively), and outperforms the existing trackers with a real-time speed of 36 Fps on a single TITAN RTX GPU. The source code and model will be released.
The preservation of cultural heritage, as mandated by the United Nations Sustainable Development Goals (SDGs), is integral to sustainable urban development. This paper focuses on the Dragon Boat Festival, a prominent event in Chinese cultural heritage, and proposes leveraging Virtual Reality (VR), to enhance its preservation and accessibility. Traditionally, participation in the festival's dragon boat races was limited to elite athletes, excluding broader demographics. Our proposed solution, named MetaDragonBoat, enables virtual participation in dragon boat racing, offering immersive experiences that replicate physical exertion through a cultural journey. Thus, we build a digital twin of a university campus located in a region with a rich dragon boat racing tradition. Coupled with three paddling techniques that are enabled by either commercial controllers or physical paddle controllers with haptic feedback, diversified users can engage in realistic rowing experiences. Our results demonstrate that by integrating resistance into the paddle controls, users could simulate the physical effort of dragon boat racing, promoting a deeper understanding and appreciation of this cultural heritage.
PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement
PDF ↗Online person re-identification services face privacy breaches from potential data leakage and recovery attacks, exposing cloud-stored images to malicious attackers and triggering public concern. The privacy protection of pedestrian images is crucial. Previous privacy-preserving person re-identification methods are unable to resist recovery attacks and compromise accuracy. In this paper, we propose an iterative method (PixelFade) to optimize pedestrian images into noise-like images to resist recovery attacks. We first give an in-depth study of protected images from previous privacy methods, which reveal that the chaos of protected images can disrupt the learning of recovery models. Accordingly, Specifically, we propose Noise-guided Objective Function with the feature constraints of a specific authorization model, optimizing pedestrian images to normal-distributed noise images while preserving their original identity information as per the authorization model. To solve the above non-convex optimization problem, we propose a heuristic optimization algorithm that alternately performs the Constraint Operation and the Partial Replacement Operation. This strategy not only safeguards that original pixels are replaced with noises to protect privacy, but also guides the images towards an improved optimization direction to effectively preserve discriminative features. Extensive experiments demonstrate that our PixelFade outperforms previous methods in resisting recovery attacks and Re-ID performance.
Existing weakly-supervised camouflaged object detection (WSCOD) methods have much difficulty in detecting accurate object boundaries due to insufficient and imprecise boundary supervision in scribble annotations. Drawing inspiration from human perception that discerns camouflaged objects by incorporating both object region and boundary information, we propose a novel Mutual Interaction Network (MiNet) for scribble-based WSCOD to alleviate the detection difficulty caused by insufficient scribbles. The proposed MiNet facilitates mutual reinforcement between region and edge cues, thereby integrating more robust priors to enhance detection accuracy. In this paper, we first construct an edge cue refinement net, featuring a core region-aware guidance module (RGM) aimed at leveraging the extracted region feature as a prior to generate the discriminative edge map. By considering both object semantic and positional relationships between edge feature and region feature, RGM highlights the areas associated with the object in the edge feature. Subsequently, to tackle the inherent similarity between camouflaged objects and the surroundings, we devise a region-boundary refinement net. This net incorporates a core edge-aware guidance module (EGM), which uses the enhanced edge map from the edge cue refinement net as guidance to refine the object boundaries in an iterative and multi-level manner. Experiments on CAMO, CHAMELEON, COD10K, and NC4K datasets demonstrate that the proposed MiNet outperforms the state-of-the-art methods.