ACM MM 2024 Papers

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.

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.

Medical image segmentation is of great significance to disease diagnosis and treatment planning. Despite multiple progresses, most present methods (1) pay insufficient attention to suppressing background noise disturbance that impacts segmentation accuracy and (2) are not efficient enough, especially when the images are of large resolutions. To address the two challenges, we turn to a traditional de-noising method and a new efficient network structure and propose BSBP-RWKV for accurate and efficient medical image segmentation. Specifically, we combine the advantages of Perona-Malik Diffusion (PMD) in noise suppression without losing boundary details and RWKV in its efficient structure, and devise the DWT-PMD RWKV Block across one of our encoder branches to preserve boundary details of lesion areas while suppressing background noise disturbance in an efficient structure. Then we feed the de-noised lesion boundary cues to our proposed Multi-Step Runge-Kutta convolutional Block to supplement the cues with more local details. We also propose a novel loss function for shape refinement that can align the shape of predicted lesion areas with GT masks in both spatial and frequency domains. Experiments on ISIC 2016 and Kvasir-SEG show the superior accuracy and efficiency of our BSBP-RWKV. Specifically, BSBP-RWKV reduces complexity of 5.8 times compared with the SOTA while also cutting down GPU memory usage by over 62.7% for each 1024 \times 1024 image during inference.

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.

Cross-modal hashing has emerged as a promising technique for retrieving relevant information across distinct media types thanks to its low storage cost and high retrieval efficiency. However, the success of most existing methods heavily relies on large-scale well-annotated datasets, which are costly and scarce in the real world due to ubiquitous labeling noise. To tackle this problem, in this paper, we propose a novel framework, termed Noise Resistance Cross-modal Hashing (NRCH), to learn hashing with noisy labels by overcoming two key challenges, i.e. noise overfitting and error accumulation. Specifically, i) to mitigate the overfitting issue caused by noisy labels, we present a novel Robust Contrastive Hashing loss (RCH) to target homologous pairs instead of noisy positive pairs, thus avoiding overemphasizing noise. In other words, RCH enforces the model focus on more reliable positives instead of unreliable ones constructed by noisy labels, thereby enhancing the robustness of the model against noise; ii) to circumvent error accumulation, a Dynamic Noise Separator (DNS) is proposed to dynamically and accurately separate the clean and noisy samples by adaptively fitting the loss distribution, thus alleviate the adverse influence of noise on iterative training. Finally, we conduct extensive experiments on four widely used benchmarks to demonstrate the robustness of our NRCH against noisy labels for cross-modal retrieval. The code is available at: https://github.com/LonganWANG-cs/NRCH.git.

Graph neural networks (GNNs) have a wide range of applications in multimedia. Recent studies have shown that Graph neural networks (GNNs) are vulnerable to link stealing attacks, which infers the existence of edges in the target GNN's training graph. Existing attacks are usually based on the assumption that links exist between two nodes that share similar posteriors; however, they fail to focus on links that do not hold under this assumption. To this end, we propose LinkThief, an improved link stealing attack that combines generalized structure knowledge with node similarity, in a scenario where the attackers' background knowledge contains partially leaked target graph and shadow graph. Specifically, to equip the attack model with insights into the link structure spanning both the shadow graph and the target graph, we introduce the idea of creating a Shadow-Target Bridge Graph and extracting edge subgraph structure features from it. Through theoretical analysis from the perspective of privacy theft, we first explore how to implement the aforementioned ideas. Building upon the findings, we design the Bridge Graph Generator to construct the Shadow-Target Bridge Graph. Then, the subgraph around the link is sampled by the Edge Subgraph Preparation Module. Finally, the Edge Structure Feature Extractor is designed to obtain generalized structure knowledge, which is combined with node similarity to form the features provided to the attack model. Extensive experiments validate the correctness of theoretical analysis and demonstrate that LinkThief still effectively steals links without extra assumptions. Our code is available at https://github.com/octopusStar218/LinkThief-MM2024.

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.

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.

Due to its high speed and low latency, DVS is frequently employed in motion deblurring. Ideally, high-quality events would adeptly capture intricate motion information. However, real-world events are generally degraded, thereby introducing significant artifacts into the deblurred results. In response to this challenge, we model the degradation of events and propose RDNet to improve the quality of image deblurring. Specifically, we first analyze the mechanisms underlying degradation and simulate paired events based on that. These paired events are then fed into the first stage of the RDNet for training the restoration model. The events restored in this stage serve as a guide for the second-stage deblurring process. To better assess the deblurring performance of different methods on real-world degraded events, we present a new real-world dataset named DavisMCR. This dataset incorporates events with diverse degradation levels, collected by manipulating environmental brightness and target object contrast. Our experiments are conducted on synthetic datasets (GOPRO), real-world datasets (REBlur), and the proposed dataset (DavisMCR). The results demonstrate that RDNet outperforms classical event denoising methods in event restoration. Furthermore, RDNet exhibits better performance in deblurring tasks compared to state-of-the-art methods. DavisMCR are available at https://github.com/Yeeesir/DVS_RDNet.

Radiology report generation aims to automatically generate clinical descriptions for radiology images, reducing the workload of radiologists. Compared to general image captioning tasks, the subtle differences in medical images and the specialized, complex nature of medical terminology limit the performance of data-driven radiology report generation. Previous research has attempted to leverage prior knowledge, such as organ-disease graphs, to enhance models' abilities to identify specific diseases and generate corresponding medical terminology. However, these methods cover only a limited number of disease types, focusing solely on disease terms mentioned in reports but ignoring their normal or abnormal attributes, which are critical to generating accurate reports. To address this issue, we propose a Divide-and-Conquer approach, named DCG, which separately constructs disease-free and disease-specific nodes within the knowledge graphs. Specifically, we extracted more comprehensive organ-disease entities from reports than previous methods and constructed disease-free and disease-specific nodes by rigorously distinguishing between normal conditions and specific diseases. This enables our model to consciously focus on abnormal information and mitigate the impact of excessively common diseases on report generation. Subsequently, the constructed graph is utilized to enhance the correlation between visual representations and disease terminology, thereby guiding the decoder in report generation. Extensive experiments conducted on benchmark datasets IU-Xray and MIMIC-CXR demonstrate the superiority of our proposed method. Code is available at https://github.com/ecoxial2007/DCG_Enhanced_distilGPT2.

Low-light image enhancement has been researched several years. However, current image restoration methods predominantly focus on recovering images from RGB images, overlooking the potential of incorporating more modalities. With the advancements in personal handheld devices, we can now easily capture images with depth information using devices such as mobile phones. The integration of depth information into image restoration is a research question worthy of exploration. Therefore, in this paper, we propose a multimodal low-light image enhancement task based on depth information and establish a dataset named LED (Low-light Image Enhanced with Depth Map), consisting of 1,365 samples. Each sample in our dataset includes a low-light image, a normal-light image, and the corresponding depth map. Moreover, for the LED dataset, we design a corresponding multimodal method, which can processes the input images and depth map information simultaneously to generate the predicted normal-light images. Experimental results and detailed ablation studies proves the efficiency of our method which exceeds previous single-modal state-of-the arts methods from relevant field.

Open-vocabulary detection (OVD) aims to detect novel objects without instance-level annotations to achieve open-world object detection at a lower cost. Existing OVD methods mainly rely on the powerful open-vocabulary image-text alignment capability of Vision-Language Pretrained Models (VLM) such as CLIP. However, CLIP is trained on image-text pairs and lacks the perceptual ability for local regions within an image, resulting in the gap between image and region representations. Directly using CLIP for OVD causes inaccurate region classification. We find the image-region gap is primarily caused by the deformation of region feature maps during region of interest (RoI) extraction. To mitigate the inaccurate region classification in OVD, we propose a new Shape-Invariant Adapter named SIA-OVD to bridge the image-region gap in the OVD task. SIA-OVD learns a set of feature adapters for regions with different shapes and designs a new adapter allocation mechanism to select the optimal adapter for each region. The adapted region representations can align better with text representations learned by CLIP. Extensive experiments demonstrate that SIA-OVD effectively improves the classification accuracy for regions by addressing the gap between images and regions caused by shape deformation. SIA-OVD achieves substantial improvements over representative methods on the COCO-OVD benchmark. The code is available at https://github.com/PKU-ICST-MIPL/SIA-OVD_ACMMM2024.

Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. However, the straightforward adoption of Mamba does not achieve satisfactory performance on point cloud tasks. In this work, we present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction, achieving superior performance, high efficiency, and scalability potential. Specifically, we propose a simple yet effective Local Norm Pooling (LNP) block to extract local geometric features. Additionally, to obtain better global features, we introduce a bidirectional SSM (bi-SSM) with both a token forward SSM and a novel backward SSM that operates on the feature channel. Extensive experimental results show that Mamba3D surpasses Transformer-based counterparts and concurrent works in multiple tasks, with or without pre-training. Notably, Mamba3D achieves multiple SoTA, including an overall accuracy of 92.6% (train from scratch) on the ScanObjectNN and 95.1% (with single-modal pre-training) on the ModelNet40 classification task, with only linear complexity. Our code and weights are available at https://github.com/xhanxu/Mamba3D.

This paper addresses the issue of cross-class domain adaptation (CCDA) in semantic segmentation, where the target domain contains both shared and novel classes that are either unlabeled or unseen in the source domain. This problem is challenging, as the absence of labels for novel classes hampers the effective solutions of both cross-domain and cross-class problems. Since Visual Language Models (VLMs) have exhibited impressive generalization across diverse data distributions and are capable of generating zero-shot predictions without requiring task-specific training examples, we propose a label alignment method by leveraging VLMs to relabel pseudo labels for novel classes. Considering that VLMs typically provide only image-level predictions, we embed a two-stage method to enable fine-grained semantic segmentation and design a threshold based on the uncertainty of pseudo labels to exclude noisy VLM predictions. To further augment the supervision of novel classes, we devise memory banks with an adaptive update scheme to effectively manage accurate VLM predictions, which are then resampled to increase the sampling probability of novel classes. Through comprehensive experiments, we demonstrate the effectiveness and versatility of our proposed method across various CCDA scenarios.

Face swapping, the technique of transferring the identity from one face to another, merges as a field with significant practical applications. However, previous swapping methods often result in visible artifacts. To address this issue, in our paper, we propose CodeSwap, a symmetrical framework to achieve face swapping with high-fidelity and realism. Specifically, our method firstly utilizes a codebook that captures the knowledge of high quality facial features. Building on this foundation, the face swapping is then converted into the code manipulation task in a code space. To achieve this, we design a Transformer-based architecture to update each code independently, which enable more precise manipulations. Furthermore, we incorporate a mask generator to achieve seamless blending of the generated face with the background of target image. A distinctive characteristic of our method is its symmetrical approach to processing both target and source images, simultaneously extracting information from each to improve the quality of face swapping. This symmetry also simplifies the bidirectional exchange of faces in a singular operation. Through extensive experiments on ClelebA-HQ and FF++, our method is proven to not only achieve efficient identity transfer but also substantially reduce the visible artifacts.

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.

Recently, the AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, for audio representation learning, existing datasets suffer from limitations in the following aspects: insufficient volume, simplistic content, and arduous collection procedures. To establish an audio dataset with high-quality captions, we propose an innovative, automatic approach leveraging multimodal inputs, such as video frames, audio streams. Specifically, we construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.5M audio-text pairs. We exploit a series of pre-trained models or APIs, to determine audio-visual synchronisation, generate image captions, object detection, or audio tags for specific videos. Subsequently, we employ LLM to paraphrase a congruent caption for each audio, guided by the extracted multi-modality clues. To demonstrate the effectiveness of the proposed dataset, we train widely used models on our dataset and show performance improvement on various downstream tasks, for example, audio-language retrieval, audio captioning, zero-shot classification. In addition, we establish a novel benchmark with environmental information and provide a benchmark for audio-text tasks.

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO.