Given a pair of images depicting a person and a garment separately, image-based 3D virtual try-on methods aim to reconstruct a 3D human model that realistically portrays the person wearing the desired garment. In this paper, we present IPVTON, a novel image-based 3D virtual try-on framework. IPVTON employs score distillation sampling with image prompts to optimize a hybrid 3D human representation, integrating target garment features into diffusion priors through an image prompt adapter. To avoid interference with non-target areas, we leverage mask-guided image prompt embeddings to focus the image features on the try-on regions. Moreover, we impose geometric constraints on the 3D model with a pseudo silhouette generated by ControlNet, ensuring that the clothed 3D human model retains the shape of the source identity while accurately wearing the target garments. Extensive qualitative and quantitative experiments demonstrate that IPVTON outperforms previous methods in image-based 3D virtual try-on tasks, excelling in both geometry and texture.
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DECIDER: Difference-aware Contrastive Diffusion Model with Adversarial Perturbations for Image Change Captioning
PDF ↗Image change captioning (ICC) poses great challenges stemming from describing subtle differences between two similar images in natural language, significantly increasing the complexity of feature extraction and cross-modal learning compared to the image captioning task. Existing ICC methods often suffer from two key challenges: 1) Massive irrelevant information of uni-image features leads to suboptimal visual difference representations; 2) Imprecise inter-modality correspondence degrades the quality of generated captions. This paper proposes a Difference-aware Contrastive Diffusion Model with Adversarial Perturbations (DECIDER) for ICC due to the excellent performance of diffusion models in image/text generation. Technically, difference-aware cross-modal learning is developed to suppress irrelevant information and learn compact yet robust visual difference representations. This is achieved by optimizing a novel objective mathematically derived from the information bottleneck principle that excels in filtering redundant features and highlighting differences. Furthermore, we propose to dynamically generate ``hard'' positive and negative samples via adversarial perturbations, which are involved in contrastive diffusion training with a tighter variational bound. This design encourages our DECIDER to excavate and construct complex correspondences between visual differences and captions, thereby improving generalization performance. Extensive experiments on four datasets demonstrate that DECIDER significantly exceeds state-of-the-art performance.
As the global population ages and the incidence of chronic diseases increases, the demand for early detection of abnormal medical conditions is increasing. Traditional health monitoring methods often require significant resources and specialized personnel, limiting their widespread use. Leveraging advancements in AI technologies, this study proposes a non-invasive method for detecting abnormal medical conditions from image data. A multimodal perception framework is introduced, integrating features from various modalities, including facial expressions and body postures, to enhance detection accuracy. The framework employs a Cascaded Squeeze-Excitation (CSE) module, consisting of Adaptive and Multi-modal Squeeze-Excitation components, to capture complex feature dependencies and improve cross-modal performance. Extensive experiments demonstrate the effectiveness of this approach, showing improved performance over existing methods. In addition, a new dataset that encompasses a wide range of medical conditions has been released, providing a valuable resource for future research in this domain.
Domain Adaptive Object Detection (DAOD) transfers knowledge from a labeled source domain to an unannotated target domain under closed-set assumption. Universal DAOD (UniDAOD) extends DAOD to handle open-set, partial-set, and closed-set domain adaptation. In this paper, we first unveil two issues: domain-private category alignment is crucial for global-level features, and the domain probability heterogeneity of features across different levels. To address these issues, we propose a novel Dual Probabilistic Alignment (DPA) framework to model domain probability as Gaussian distribution, enabling the heterogeneity domain distribution sampling and measurement. The DPA consists of three tailored modules: the Global-level Domain Private Alignment (GDPA), the Instance-level Domain Shared Alignment (IDSA), and the Private Class Constraint (PCC). GDPA utilizes the global-level sampling to mine domain-private category samples and calculate alignment weight through a cumulative distribution function to address the global-level private category alignment. IDSA utilizes instance-level sampling to mine domain-shared category samples and calculates alignment weight through Gaussian distribution to conduct the domain-shared category domain alignment to address the feature heterogeneity. The PCC aggregates domain-private category centroids between feature and probability spaces to mitigate negative transfer. Extensive experiments demonstrate that our DPA outperforms state-of-the-art UniDAOD and DAOD methods across various datasets and scenarios, including open, partial, and closed sets.
The success of visual tracking has been largely driven by datasets with manual box annotations. However, these box annotations require tremendous human effort, limiting the scale and diversity of existing tracking datasets. In this work, we present a novel Self-Supervised Tracking framework, named SSTrack, designed to eliminate the need of box annotations. Specifically, a decoupled spatio-temporal consistency training framework is proposed to learn rich target information across timestamps through global spatial localization and local temporal association. This allows for the simulation of appearance and motion variations of instances in real-world scenarios. Furthermore, an instance contrastive loss is designed to learn instance-level correspondences from a multi-view perspective, offering robust instance supervision without additional labels. This new design paradigm enables SSTrack to effectively learn generic tracking representations in a self-supervised manner, while reducing reliance on extensive box annotations. Extensive experiments on nine benchmark datasets demonstrate that SSTrack surpasses SOTA self-supervised tracking methods, achieving an improvement of more than 25.3%, 20.4%, and 14.8% in AUC (AO) score on the GOT10K, LaSOT, TrackingNet datasets, respectively.
OODML: Whole Slide Image Classification Meets Online Pseudo-Supervision and Dynamic Mutual Learning
PDF ↗Bag-label-based multi-instance learning (MIL) has demonstrated significant performance in whole slide image (WSI) analysis, particularly in pseudo-label-based learning schemes. However, due to inaccurate feature representation and interference, existing MIL methods often yield unreliable pseudo-labels, which spawn undesired predictions. To address these issues, we propose an Online Pseudo-Supervision and Dynamic Mutual Learning (OODML) framework that enhances pseudo-label generation and feature representation while exploring their mutual learning to improve bag-level prediction. Specifically, we design an Adaptive Memory Bank (AMB) to collect the most informative components of the current WSI. We also introduce a Self-Progressive Feature Fusion (SPFF) module that integrates label-related historical information from the AMB with current semantic variations, thereby enhancing the representation of pseudo-bag tokens. Furthermore, we propose a Decision Revision Pseudo-Label (DRPL) generation scheme to explore intrinsic connections between pseudo-bag representations and bag-label predictions, resulting in more reliable pseudo-label generation. To alleviate redundant and ambiguous representations, the class-wise prior of pseudo-label prediction is borrowed to facilitate label-related feature learning and to update the AMB, forming a mutual refinement between feature representation and pseudo-label generation. Additionally, a Dynamic Decision-Making (DDM) module is developed to harmonize explicit and implicit representations of bag information for more robust decision-making. Extensive experiments on four datasets demonstrate that our OODML surpasses the state-of-the-art by 3.3% and 6.9% on the CAMELYON16 and TCGA Lung datasets.
Visual storytelling involves generating a sequence of coherent frames from a textual storyline while maintaining consistency in characters and scenes. Existing autoregressive methods, which rely on previous frame-sentence pairs, struggle with high memory usage, slow generation speeds, and limited context integration. To address these issues, we propose ContextualStory, a novel framework designed to generate coherent story frames and extend frames for visual storytelling. ContextualStory utilizes Spatially-Enhanced Temporal Attention to capture spatial and temporal dependencies, handling significant character movements effectively. Additionally, we introduce a Storyline Contextualizer to enrich context in storyline embedding, and a StoryFlow Adapter to measure scene changes between frames for guiding the model. Extensive experiments on PororoSV and FlintstonesSV datasets demonstrate that ContextualStory significantly outperforms existing SOTA methods in both story visualization and continuation.
Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.
When Shadow Removal Meets Intrinsic Image Decomposition: A Joint Learning Framework Using Unpaired Data
PDF ↗We present a framework that achieves shadow removal by learning intrinsic image decomposition (IID) from unpaired shadow and shadow-free images. Although it is well-known that intrinsic images, \ie, illumination and reflectance, are highly beneficial to shadow removal, IID is rarely adopted by previous work due to its inherent ambiguity and the scarcity of training data. However, we find that by properly coupling shadow removal and IID into a joint learning framework, they can reinforce each other and enable promising results on both tasks, even with unpaired training data. Our framework is comprised of an IID network for separating the shadow input image into illumination and reflectance, and an illumination recovery network for predicting shadow-free illumination with which we are able to produce the shadow removal output by recombining with the estimated reflectance. We perform extensive experiments on various benchmark datasets to demonstrate the effectiveness of our method in shadow removal, and also showcase our advantage over previous IID methods in handling images with complex shadows.
LIDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supervising 3D object detection using only semantic labels. However, the challenge arises due to the incomplete geometry structure and boundary ambiguity of point cloud instances, leading to inaccurate pseudo-labels and poor detection results. To address these challenges, we propose a novel method, named Seg2Box. We first introduce a Multi-Frame Multi-Scale Clustering (MFMS-C) module, which leverages the spatio-temporal consistency of point clouds to generate accurate box-level pseudo-labels. Additionally, the Semantic-Guiding Iterative-Mining Self-Training (SGIM-ST) module is proposed to enhance the performance by progressively refining the pseudo-labels and mining the instances without generating pseudo-labels. Experiments on the Waymo Open Dataset and nuScenes Dataset show that our method significantly outperforms other competitive methods by 23.7% and 10.3% in mAP, respectively. The results demonstrate the great label-efficient potential and advancement of our method.
Although diffusion-based techniques have shown remarkable success in image generation and editing tasks, their abuse can lead to severe negative social impacts. Recently, some works have been proposed to provide defense against the abuse of diffusion-based methods. However, their protection may be limited in specific scenarios by manually defined prompts or the stable diffusion (SD) version. Furthermore, these methods solely focus on tuning methods, overlooking editing methods that could also pose a significant threat. In this work, we propose Anti-Diffusion, a privacy protection system designed for general diffusion-based methods, applicable to both tuning and editing techniques. To mitigate the limitations of manually defined prompts on defense performance, we introduce the prompt tuning (PT) strategy that enables precise expression of original images. To provide defense against both tuning and editing methods, we propose the semantic disturbance loss (SDL) to disrupt the semantic information of protected images. Given the limited research on the defense against editing methods, we develop a dataset named Defense-Edit to assess the defense performance of various methods. Experiments demonstrate that our Anti-Diffusion achieves superior defense performance across a wide range of diffusion-based techniques in different scenarios.
Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods.
Breaking Information Isolation: Accelerating MRI via Inter-sequence Mapping and Progressive Masking
PDF ↗Deep unfolding network (DUN) has shed new light on multi-sequence MRI reconstruction, providing both high interpretability and acceptable performance. However, current approaches still suffer from the plight of information isolation, i.e., learning features of multi-suquences individually and leaving the mask departed from model updating. In this work, we propose a new unfolding solution, namely Information-coupled MRI Acceleration (IMA), to address the isolation issue. Concretely, two specific mechanisms are presented. On the one hand, the latent connections across different sequences are explicitly molded via two auxiliary matrices. While the first matrix is meticulously engineered to assemble the spatial details, the second one hammers at capturing the depth information conditioned on the enriched channels. On the other hand, following a deep analysis on the non-uniform distribution in low- and high-frequency components of the given mask, we elaborate a new unfolding flow using a progressive masking scheme, featuring a dilation-contraction mechanism during forward propagation of successive stages. Massive experiments are conducted under various sampling patterns and acceleration rates, whose results demonstrate that, without any sophisticated architectures, our IMA outperforms the current cutting-edge methods both visually and numerically.
Portraits often suffer from specular highlights due to factors like skin oiliness, lighting conditions, and shooting angles, which degrade aesthetics and affect downstream tasks. Thus, portrait highlight removal is imperative. Previous methods struggle to remove highlights and achieve high-fidelity restoration of disturbed regions simultaneously. In this work, we propose a novel patch-based diffusion model for this task, named PHR-DIFF. Specifically, in the training, we present a patchify training strategy that divides the portrait into equal-sized patches and performs diffusion on these patches individually. This patchify can extract more compact facial features and reduce training costs. Besides, to learn the global coherence of the face, we propose a patch-residual approach. It encodes the full-resolution highlight-free portrait into latent features, which are further used as residual terms to constrain the forward training. In the sampling, we remove portrait highlights in a patch-wise manner and propose a Patch-Aware Highlight Removal (PAHR) mechanism. PAHR leverages features from non-highlight regions to effectively guide the patch-wise removal of highlight components. Experimental results on multiple public datasets demonstrate that PHR-DIFF removes highlights more cleanly and avoids artifacts.
Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name "immunized". Recent work on model immunization focuses on the single-concept setting. However, in real-world situations, models need to be immunized against multiple concepts. To address this gap, we propose an immunization algorithm that, simultaneously, learns a single "difficult initialization" for adaptation methods over a set of concepts. We achieve this by incorporating a differentiable merging layer that combines a set of model weights adapted over multiple concepts. In our experiments, we demonstrate the effectiveness of multi-concept immunization by generalizing prior work's experiment setup of re-learning and personalization adaptation to multiple concepts.
Heterogeneous Prompt-Guided Entity Inferring and Distilling for Scene-Text Aware Cross-Modal Retrieval
PDF ↗In cross-modal retrieval, comprehensive image understanding is vital while the scene text in images can provide fine-grained information to understand visual semantics. Current methods fail to make full use of scene text. They suffer from the semantic ambiguity of independent scene text and overlook the heterogeneous concepts in image-caption pairs. In this paper, we propose a heterogeneous prompt-guided entity inferring and distilling (HOPID) network to explore the nature connection of scene text in images and captions and learn a property-centric scene text representation. Specifically, we propose to align scene text in images and captions via heterogeneous prompt, which consists of visual and text prompt. For text prompt, we introduce the discriminative entity inferring module to reason key scene text words from captions, while visual prompt highlights the corresponding scene text in images. Furthermore, to secure a robust scene text representation, we design a perceptive entity distilling module that distills the beneficial information of scene text at a fine-grained level. Extensive experiments show that the proposed method significantly outperforms existing approaches on two public cross-modal retrieval benchmarks.
Referring Multi-Object Tracking (RMOT) aims to track multiple objects based on a provided language expression. Although prior studies have sought to accomplish this by integrating an textual module into the multi-object tracker, these methods combine text and image features in a basic way, neglecting the importance of text features. In this study, we propose a Hierarchical Fine-grained text-image Fusion tracker, named HFF-Tracker, which can perform fine-grained fusion of pixel-level visual features and text features across various semantic levels. Specifically, we have devised a Hierarchical Multi-Modal Fusion (HMMF) module to merge text and image features at an early stage in a hierarchical and detailed manner. The Text-Guided Decoder (TGD) is designed to provide the query with prior semantic information during the decoding process. Additionally, we have crafted a Text-Guided Prediction Head (TGPH) that utilizes text information to enhance the performance of the prediction head. Furthermore, we have implemented an adaptive Look-Back training strategy to maximize the utilization of valuable labeled data. Extensive experiments on the Refer-KITTI dataset and the Refer-KITTI-V2 dataset demonstrate that our proposed HFF-Tracker outperforms other state-of-the-art methods with remarkable margins.
Person re-identification (Re-ID) is crucial for intelligent surveillance systems, facilitating the identification of individuals across multiple camera views. While significant advancements have been made for daytime scenarios, ensuring reliable Re-ID performance during nighttime remains a significant challenge. Given the cost and limited accessibility of infrared cameras, we investigate a critical question: Can RGB cameras be effectively utilized for accurate Re-ID during nighttime? To address this, we introduce NightReID, a large-scale RGB Re-ID dataset collected from a real-world nighttime surveillance system. NightReID includes 1,500 identities and over 53,000 images, capturing diverse scenes with complex lighting and adverse weather conditions. This rich dataset provides a valuable benchmark for advancing nighttime Re-ID research. Moreover, we propose the Enhancement, Denoising, and Alignment (EDA) framework with two novel modules to enhance nighttime Re-ID performance. First, an unsupervised Image Enhancement and Denoising (IED) method is designed to improve the quality of nighttime images, preserving critical details while removing noise without requiring paired ground truth. Second, we introduce Data Distribution Alignment (DDA) through statistical priors, aligning the distributions between pre-training data and nighttime data to mitigate domain shift. Extensive experiments on multiple nighttime Re-ID datasets demonstrate the significance of NightReID and validate the efficacy, flexibility, and applicability of the EDA framework.
Event-based semantic segmentation (ESS) has attracted researchers' attention recently, as event cameras can solve problems such as under/over-exposure or motion blur that are difficult for RGB cameras to handle. However, event data are noisy and sparse, resulting in difficulties for the model to locate and extract reliable cues from their sparse representations, especially when performing pixel-level tasks. In this paper, we propose a novel framework ESEG to alleviate the dilemma. Given that event signals relate closely to moving edges, instead of proposing complex structures to expect them to recognize those reliable edge regions behind event signals on their own, we introduce the explicit edge-semantic supervision as a reference to let the ESS model globally optimize semantics, considering the high confidence of event data in edge regions. In addition, we propose a fusion module named Density-Aware Dynamic-Window Cross Attention Fusion (D\textsuperscript{2}CAF), in which the density perception, cross-attention, and dynamic window masking mechanisms are jointly imposed to optimize edge-dense feature fusion, leveraging the characteristics of event cameras. Experimental results on DSEC and DDD17 datasets demonstrate the efficacy of the ESEG framework and its core designs.
Open-vocabulary 3D object detection (OV-3DOD) aims at localizing and classifying novel objects beyond closed sets. The recent success of vision-language models (VLMs) has demonstrated their remarkable capabilities to understand open vocabularies. Existing works that leverage VLMs for 3D object detection (3DOD) generally resort to representations that lose the rich scene context required for 3D perception. To address this problem, we propose in this paper a hierarchical framework, named HCMA, to simultaneously learn local object and global scene information for OV-3DOD. Specifically, we first design a Hierarchical Data Integration (HDI) approach to obtain coarse-to-fine 3D-image-text data, which is fed into a VLM to extract object-centric knowledge. To facilitate the association of feature hierarchies, we then propose an Interactive Cross-Modal Alignment (ICMA) strategy to establish effective intra-level and inter-level feature connections. To better align features across different levels, we further propose an Object-Focusing Context Adjustment (OFCA) module to refine multi-level features by emphasizing object-related features. Extensive experiments demonstrate that the proposed method outperforms SOTA methods on the existing OV-3DOD benchmarks. It also achieves promising OV-3DOD results even without any 3D annotations.