Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for urban applications. However, existing methods often overlook significant domain gaps across diverse urban landscapes, primarily focusing on enhancing retrieval performance within single domains. To tackle this issue, we present UrbanCross, a new framework for cross-domain satellite image-text retrieval. UrbanCross leverages a high-quality, cross-domain dataset enriched with extensive geo-tags from three countries to highlight domain diversity. It employs the Large Multimodal Model (LMM) for textual refinement and the Segment Anything Model (SAM) for visual augmentation, achieving fine-grained alignment of images, segments and texts, yielding a 10% improvement in retrieval performance. Additionally, UrbanCross incorporates an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module, progressively enhancing adaptability across various domains. Extensive experiments confirm UrbanCross's superior efficiency in retrieval and adaptation to new urban environments, demonstrating an average performance increase of 15% over its version without domain adaptation mechanisms, effectively bridging the domain gap. Our code and dataset are publicly accessible at https://github.com/siruzhong/UrbanCross.
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Nowadays, the abuse of AI-generated content (AIGC), especially the facial images known as deepfake, on social networks has raised severe security concerns, which might involve the manipulations of both visual and audio signals. For multimodal deepfake detection, previous methods usually exploit forgery-relevant knowledge to fully finetune Vision transformers (ViTs) and perform cross-modal interaction to expose the audio-visual inconsistencies. However, these approaches may undermine the prior knowledge of pretrained ViTs and ignore the domain gap between different modalities, resulting in unsatisfactory performance. To tackle these challenges, in this paper, we propose a new framework, i.e., Forgery-aware Audio-distilled Multimodal Learning (FRADE), for deepfake detection. In FRADE, the parameters of pretrained ViT are frozen to preserve its prior knowledge, while two well-devised learnable components, i.e., the Adaptive Forgery-aware Injection (AFI) and Audio-distilled Cross-modal Interaction (ACI), are leveraged to adapt forgery relevant knowledge. Specifically, AFI captures high-frequency discriminative features on both audio and visual signals and injects them into ViT via the self-attention layer. Meanwhile, ACI employs a set of latent tokens to distill audio information, which could bridge the domain gap between audio and visual modalities. The ACI is then used to well learn the inherent audio-visual relationships by cross-modal interaction. Extensive experiments demonstrate that the proposed framework could outperform other state-of-the-art multimodal deepfake detection methods under various circumstances.
Cross-view consensus representation plays a critical role in hyperspectral images (HSIs) clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract contextual consensus representation. However, these methods have a fatal flaw: contrastive learning may treat similar heterogeneous views as positive sample pairs and dissimilar homogeneous views as negative sample pairs. At the same time, the data representation via self-supervised contrastive loss is not specifically designed for clustering. Thus, to tackle this challenge, we propose a novel multi-view clustering method, i.e., Enhanced Multi-View Contrastive Clustering (EMVCC). First, the spatial multi-view is designed to learn the diverse features for contrastive clustering, and the globally relevant information of spectrum-view is extracted by Transformer, enhancing the spatial multi-view differences between neighboring samples. Then, a joint self-supervised loss is designed to constrain the consensus representation from different perspectives to efficiently avoid false negative pairs. Specifically, to preserve the diversity of multi-view information, the features are enhanced by using probabilistic contrastive loss, and the data is projected into a semantic representation space, ensuring that the similar samples in this space are closer in distance. Finally, we design a novel clustering loss that aligns the view feature representation with high confidence pseudo-labels for promoting the network to learn cluster-friendly features. In the training process, the joint self-supervised loss is used to optimize the cross-view features.Abundant experiment studies on numerous benchmarks verify the superiority of EMVCC in comparison to some state-of-the-art clustering methods. The codes are available at https://github.com/YiLiu1999/EMVCC.
While RAW images are efficient for image editing and perception tasks, their large size can strain camera storage and bandwidth. Reconstruction methods of RAW images from sRGB data typically require additional metadata from the RAW image, which increases camera processing computations. To address this problem, we propose using Prior Meta as a reference to reconstruct the RAW data instead of relying on per-image metadata. Prior metadata is extracted offline from reference RAW images, which are usually part of the training dataset and have similar scenes and light conditions as the target image. With this prior metadata, the camera does not need to provide any extra processing other than the sRGB images, and our model can autonomously find the desired prior information. To achieve this, we design a three-step pipeline. First, we build a pixel searching network that can find the most similar pixels in the reference RAW images as prior information. Then, in the second step, we compress the large-scale reference images to about 0.02% of their original size to reduce the searching cost. Finally, in the last step, we develop a neural network reconstructor to reconstruct the high-fidelity RAW images. Our model achieves comparable, and even better, performance than RAW reconstruction methods based on metadata.
SpecGaussian with Latent Features: A High-quality Modeling of the View-dependent Appearance for 3D Gaussian Splatting
PDF ↗Recently, the 3D Gaussian Splatting (3D-GS) method has achieved great success in novel view synthesis, providing real-time rendering while ensuring high-quality rendering results. However, this method faces challenges in modeling specular reflections and handling anisotropic appearance components, especially in dealing with view-dependent color under complex lighting conditions. Additionally, 3D-GS uses spherical harmonic to learn the color representation, which has limited ability to represent complex scenes. To overcome these challenges, we introduce Lantent-SpecGS, an approach that utilizes a universal latent neural descriptor within each 3D Gaussian. This enables a more effective representation of 3D feature fields, including appearance and geometry. Moreover, two parallel CNNs are designed to decoder the splatting feature maps into diffuse color and specular color separately. A mask that depends on the viewpoint is learned to merge these two colors, resulting in the final rendered image. Experimental results demonstrate that our method obtains competitive performance in novel view synthesis and extends the ability of 3D-GS to handle intricate scenarios with specular reflections.
Fully test-time adaptation aims to adapt a network model online based on sequential analysis of input samples during the inference stage. We observe that, when applying a transformer network model into a new domain, the self-attention profiles of image samples in the target domain deviate significantly from those in the source domain, which results in large performance degradation during domain changes. To address this important issue, we propose a new structure for the self-attention modules in the transformer. Specifically, we incorporate three domain-conditioning vectors, called domain conditioners, into the query, key, and value components of the self-attention module. We learn a network to generate these three domain conditioners from the class token at each transformer network layer. We find that, during fully online test-time adaptation, these domain conditioners at each transform network layer are able to gradually remove the impact of domain shift and largely recover the original self-attention profile. Our extensive experimental results demonstrate that the proposed domain-conditioned transformer significantly improves the online fully test-time domain adaptation performance and outperforms existing state-of-the-art methods by large margins. The code is available at https://github.com/yushuntang/DCT.
In this work, we introduce a novel approach to single-source domain generalization (SDG) in medical imaging, focusing on overcoming the challenge of style variation in out-of-distribution (OOD) domains without requiring domain labels or additional generative models. We propose a Universal Frequency Perturbation framework for SDG termed as UniFreqSDG, that performs hierarchical feature-level frequency domain perturbations, facilitating the model's ability to handle diverse OOD styles. Specifically, we design a learnable spectral perturbation module that adaptively learns the frequency distribution range of samples, allowing for precise low-frequency (LF) perturbation. This adaptive approach not only generates stylistically diverse samples but also preserves domain-invariant anatomical features without the need for manual hyperparameter tuning. Then, the frequency features before and after perturbation are decoupled and recombined through the Content Preservation Reconstruction operation, effectively preventing the loss of discriminative content information. Furthermore, we introduce the Active Domain-variance Inducement Loss to encourage effective perturbation in the frequency domain while ensuring the sufficient decoupling of domain-invariant and domain-style features. Extensive experiments demonstrate that UniFreqSDG increases the dice score by an average of 7.47% (from 77.98% to 85.45%) on the fundus dataset and 4.99% (from 71.42% to 76.73%) on the prostate dataset compared to the state-of-the-art approaches.
Few-Shot Industrial Anomaly Detection (FS-IAD) has drawn great attention most recently since data efficiency and the ability to design algorithms for fast migration across products have become the main concerns. The difficulty of memory-based IAD in low-data regime primarily lies in inefficient measurement between the memory bank and query images. We address such a pivotal issue from a new perspective of optimal matching between features of image regions. Taking the unbalanced nature of query features into consideration, we adopt Conditional Transport (CT) as a metric to compute the structural distance between representations of the two sets to determine feature relevance. CT distance generates the optimal matching flows between unbalanced structural elements that achieve the minimum matching cost, which can be directly used for IAD since it well reflects the differences of query images compared with the normal memory. Realizing the fact that query images usually come one-by-one or batch-by-batch, we further propose an Online Conditional Transport (OCT) by making full use of the current and historical query images for IAD via simultaneously calibrating the memory bank using the online query images and matching features between the calibrated memory and the current query image. Go one step further, for sparse foreground products, we employ a predominant segment model to implement Foreground-aware OCT (FOCT) for improving the effectiveness and efficiency of OCT by forcing the model to pay more attention to diverse targets rather than redundant backgrounds when calibrating the memory bank. FOCT can improve the diversity of calibrated memory, which is critical for robust FS-IAD in practice. Besides, FOCT is flexible since it can be friendly plugged and played with any pre-trained backbones, such as WRN, and any pre-trained segment models, such as SAM. The effectiveness and efficiency of our model is demonstrated across diverse datasets, including benchmarks of MVTec and MPDD, achieving SOTA performance.
Scene synthesis has gained significant attention recently, and interactive scene synthesis focuses on yielding scenes according to user preferences. Existing literature either generates floor plans or scenes according to the floor plans. The system proposed in this paper generates scenes over floor plans in real-time. Given an initial scene, the only interaction a user needs is changing the room shapes. Our framework splits/merges rooms and adds/rearranges/removes objects for each transient moment during interactions. A systematic pipeline achieves our framework by compressing objects' arrangements over modified room shapes in a transient moment, thus enabling real-time performances. We also propose elastic boxes that indicate how objects should be arranged according to their continuously changed contexts, such as room shapes and other objects. Through a few interactions, a floor plan filled with object layouts is generated concerning user preferences on floor plans and object layouts according to floor plans. Experiments show that our framework is efficient at user interactions and plausible for synthesizing 3D scenes.
Neural implicit representations have recently revolutionized simultaneous localization and mapping (SLAM), giving rise to a groundbreaking paradigm known as NeRF-based SLAM. However, existing methods often fall short in accurately estimating poses and reconstructing scenes. This limitation largely stems from their reliance on volume rendering techniques, which oversimplify the modeling process. In this paper, we introduce a novel neural implicit SLAM system named SAR-SLAM to address these shortcomings. Our approach reconstructs Neural Radiance Fields (NeRFs) using a self-attentive architecture and represents scenes through neural point cloud encoding. Unlike previous NeRF-based SLAM methods, which depend on traditional volume rendering equations for scene representation and view synthesis, our method employs a self-attentive rendering framework with the Transformer architecture during mapping and tracking stages. To enable incremental mapping, we anchor scene features within a neural point cloud, striking a balance between estimation accuracy and computational cost. Experimental results on three challenging datasets show the superior performance and robustness of our SAR-SLAM compared to recent NeRF-based SLAM systems. The code will be released.
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based black-box adversarial attacks estimate adversarial gradients based on the returned probability vectors of the target model for a sequence of queries. During this process, the queries made to the target model are intermediate adversarial examples crafted at the previous attack step, which share high similarities in the pixel space. Motivated by this observation, stateful detection methods have been proposed to detect and reject query-based attacks. While demonstrating promising results, these methods either have been evaded by more advanced attacks or suffer from low efficiency in terms of the number of shots (queries) required to detect different attacks. Arguably, the key challenge here is to assign high similarity scores for any two intermediate adversarial examples perturbed from the same clean image. To address this challenge, we propose a novel Adversarial Contrastive Prompt Tuning (ACPT) method to robustly fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. With ACPT, we further introduce a detection framework AdvQDet that can detect 7 state-of-the-art query-based attacks with >99% detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks. Code is available at https://github.com/xinwong/AdvQDet.
Lightweight models play an important role in real-life applications, especially in the recent mobile device era. However, due to limited network scale and low-quality images, the performance of lightweight models on Scene Text Recognition (STR) tasks is still much to be improved. Recently, contrastive learning has shown its power in many areas, with promising performances without additional computational cost. Based on these observations, we propose a new efficient and effective frame-level contrastive learning (FLCL) framework for lightweight STR models. The FLCL framework consists of a backbone to extract basic features, a Text Perceiver Module (TPM) to focus on text-relevant representations, and a FLCL loss to update the network. The backbone can be any feature extraction architecture. The TPM is an innovative Mamba-based structure that is designed to suppress features irrelevant to the text content from the backbone. Unlike existing word-level contrastive learning, we look into the nature of the STR task and propose the frame-level contrastive learning loss, which can work well with the famous Connectionist Temporal Classification loss. We conduct experiments on six well-known STR benchmarks as well as a new low-quality dataset. Compared to vanilla contrastive learning and other non-parameter methods, the FLCL framework significantly outperforms others on all datasets, especially the low-quality dataset. In addition, character feature visualization demonstrates that the proposed method can yield more discriminative character features for visually similar characters, which also substantiates the efficacy of the proposed methods. Codes and the low-quality dataset will be available soon.
Estimating 3D human poses from monocular images is an important research area with many practical applications. However, the depth ambiguity of 2D solutions limits their accuracy in actions where occlusion exits or where slight centroid shifts can result in significant 3D pose variations. In this paper, we introduce a novel multimodal approach to mitigate the depth ambiguity inherent in monocular solutions by integrating spatial-aware pressure information. We first establish a data collection system with a pressure mat and a monocular camera, and construct a large-scale multimodal human activity dataset comprising over 600,000 frames of motion data. Utilizing this dataset, we propose a pressure image reconstruction network to extract pressure priors from monocular images. Subsequently, we introduce a Transformer-based multimodal pose estimation network to combine pressure priors with monocular images, achieving a world mean per joint position error of 51.6mm, outperforming state-of-the-art methods. Extensive experiments demonstrate the effectiveness of our multimodal 3D human pose estimation method across various actions and joints, highlighting the significance of spatial-aware pressure in improving the accuracy of monocular-vision-based methods. Our dataset is available at: https://github.com/LishuangZhan/SATPose.
Underwater images, often plagued by complex degradation, pose significant challenges for image enhancement. To address these challenges, the paper redefines underwater image enhancement as an image decomposition problem and proposes a deep invertible neural network (INN) that accurately predicts both the latent image and the degradation effects. Instead of using an explicit formation model to describe the degradation process, the INN adheres to the constraints of the image decomposition model, providing necessary regularization for model training, particularly in the absence of supervision on degradation effects. Taking into account the diverse scales of degradation factors, the INN is structured on a multi-scale basis to effectively manage the varied scales of degradation factors. Moreover, the INN incorporates several asymmetric design elements that are specifically optimized for the decomposition model and the unique physics of underwater imaging. Comprehensive experiments show that our approach provides significant performance improvement over existing methods.
Moving infrared small target detection, crucial in contexts like traffic management and maritime rescue, encounters challenges from factors such as complex backgrounds, target occlusion, camera shake, and motion blur. Existing algorithms fall short in comprehensively addressing these issues by exploring hybrid modeling, impeding generalization in complex and dynamic motion scenes. In this paper, we propose a hybrid modeling method for moving infrared small target detection via smoothed-particle hydrodynamics (SPH) and Markov decision processes (MDP). SPH can simulate the motion trajectories of targets and background scenes, while MDP can optimize detection system strategies for optimal action selection based on contexts and target states. Specifically, we develop an SPH-inspired image-level enhancement algorithm which models the image sequence of infrared video as a 3D spatiotemporal graph in SPH. Enhancing the spatiotemporal information of the target using the designed sliding window and fluid dynamics formula. In addition, we design an MDP-guided temporal feature perception module. This module selects reference frames, aggregates features from both reference frames and the current frame. The previous and current frames are modeled as an MDP tailored for multi-frame infrared small target detection tasks, aiding in detecting the current frame. Conducted extensive experiments on two public dataset: DAUB and DATR, the proposed network surpasses the state-of-the-art methods in terms of objective metrics and visual quality.
With the development of depth sensors and 3D vision, the vulnerability of 3D point cloud models has garnered heightened concern. Almost all existing 3D attackers are deployed in the white-box setting, where they access the model details and directly optimize coordinate-wise noises to perturb 3D objects. However, realistic 3D applications would not share any model information (model parameters, gradients, etc.) with users. Although a few recent works try to explore the black-box attack, they still achieve limited attack success rates (ASR) and fail to generate high-quality adversarial samples. In this paper, we focus on designing a transfer-based black-box attack method, called Transferable Frequency-aware 3D GAN, to delve into achieving a high black-box ASR by improving the adversarial transferability while making the adversarial samples more imperceptible. Considering that the 3D imperceptibility depends on whether the shape of the object is distorted, we utilize the spectral tool with the GAN design to explicitly perceive and preserve the 3D geometric structures. Specifically, we design the Graph Fourier Transform (GFT) encoding layer in the GAN generator to extract the geometries as guidance, and develop a corresponding Inverse-GFT decoding layer to decode latent features with this guidance to reconstruct high-quality adversarial samples. To further improve the transferability, we develop a dual learning scheme of discriminator from both frequency and feature perspectives to constrain the generator via adversarial learning. Finally, imperceptible and transferable perturbations are rapidly generated by our proposed attack. Experimental results demonstrate that our attack method achieves the highest transfer ASR while exhibiting stronger imperceptibility.
Swarical, a Swar m-based hierarchical localization technique, enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. Its accuracy depends on the physical hardware (sensors) of FLSs used to track neighboring FLSs to localize themselves. It uses the specification of the sensors to convert mesh files into point clouds that enable a swarm of FLSs to localize at the highest accuracy afforded by their sensors. Swarical considers a heterogeneous mix of FLSs with different orientations for their tracking sensors, ensuring a line of sight between a localizing FLS and its anchor FLS. We present an implementation using Raspberry cameras and ArUco markers. A comparison of Swarical with a state of the art decentralized localization technique shows that it is as accurate and more than 2x faster.
The domain of image restoration encompasses a wide array of highly effective models (e.g., SwinIR, CODE, DnCNN), each exhibiting distinct advantages in either efficiency or performance. Selecting and deploying these models necessitate careful consideration of resource limitations. While some studies have explored dynamic restoration through the integration of an auxiliary network within a unified framework, these approaches often fall short in practical applications due to the complexities involved in training, retraining, and hyperparameter adjustment, as well as limitations as being totally controlled by auxiliary network and biased by training data. To address these challenges, we introduce FlexIR: a flexible and manipulable framework for image restoration. FlexIR is distinguished by three components: a meticulously designed hierarchical branch network enabling dynamic output, an innovative progressive self-distillation process, and a channel-wise evaluation method to enhance knowledge distillation efficiency. Additionally, we propose two novel inference methodologies to fully leverage FlexIR, catering to diverse user needs and deployment contexts. Through this framework, FlexIR achieves unparalleled performance across all branches, allowing users to navigate the trade-offs between quality, cost, and efficiency during the inference phase. Crucially, FlexIR employs a dynamic mechanism powered by a non-learning metric independent of training data, ensuring that FlexIR is entirely under the direct control of the user. Comprehensive experimental evaluations validate FlexIR's flexibility, manipulability, and cost-effectiveness, showcasing its potential for straightforward adjustments and quick adaptations across a range of scenarios.
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing labels, leveraging VLP prompt-tuning technology. However, they usually cannot match text and vision features well, due to complicated semantics gaps and missing labels in a multi-label image. To tackle this challenge, we propose Text-Region Matching for optimizing Multi-Label prompt tuning, namely TRM-ML, a novel method for enhancing meaningful cross-modal matching. Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual representations in a one-to-one matching manner. Concurrently, we further introduce multimodal contrastive learning to narrow the semantic gap between textual and visual modalities and establish intra-class and inter-class relationships. Additionally, to deal with missing labels, we propose a multimodal category prototype that leverages intra- and inter-category semantic relationships to estimate unknown labels, facilitating pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC, Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art methods by a significant margin. Our code is available here.
Recent advances in multimodal artificial intelligence have greatly improved the integration of vision-language-audio cues to enrich the content creation process. Inspired by these developments, in this paper, we first integrate audio into the face inpainting task to facilitate identity manipulation. Our main insight is that a person's voice carries distinct identity markers, such as age and gender, which provide an essential supplement for identity-aware face inpainting. By extracting identity information from audio as guidance, our method can naturally support tasks of identity preservation and identity swapping in face inpainting. Specifically, we introduce a dual-stream network architecture comprising a face branch and an audio branch. The face branch is tasked with extracting deterministic information from the visible parts of the input masked face, while the audio branch is designed to capture heuristic identity priors from the speaker's voice. The identity codes from two streams are integrated using a multi-layer perceptron (MLP) to create a virtual unified identity embedding that represennts comprehensive identity features. In addition, to explicitly exploit the information from audio, we introduce an audio-face generator to generate an 'fake' audio face directly from audio and fuse the multi-scale intermediate features from the audio-face generator into face inpainting network through an audio-visual feature fusion (AVFF) module. Extensive experiments demonstrate the positive impact of extracting identity information from audio on face inpainting task, especially in identity preservation.