ACM MM 2024 论文列表

Snapshot spectral compressive imaging can capture spectral information across multiple wavelengths in one imaging. The coded aperture snapshot spectral imaging (CASSI) method, aims to recover 3D spectral cubes from 2D measurements. Most existing approaches employ a deep unfolding framework based on Transformer, which alternately address a data subproblem and a prior subproblem. However, these frameworks lack flexibility regarding the sensing matrix and inter-stage interactions. In addition, the quadratic computational complexity of global Transformer and the restricted receptive field of local Transformer impact reconstruction efficiency and accuracy. In this paper, we propose a dynamic deep unfolding network with mamba for compressive spectral imaging, called VmambaSCI. We integrate spatial-spectral information from the sensing matrix into the data module and utilizes spatial adaptive operations in the stage interaction of the prior module. Furthermore, recognizing that the imaging process causes aliasing of spatial and spectral information, we develop a dual-domain scanning mamba (DSMamba), featuring a novel spatial-channel scanning method for enhanced efficiency and accuracy. To our knowledge, VmambaSCI is the first Mamba-based model for compressive spectral imaging. Experimental results on the public databases, CAVE and KAIST, demonstrate the superiority of the proposed VmambaSCI over the state-of-the-art approaches.

Stereo image super-resolution (stereoSR) strives to improve the quality of super-resolution by leveraging the auxiliary information provided by another perspective. Most approaches concentrate on refining module design, and stacking massive network blocks to extract and integrate information. Although there have been advancements, the memory and computation costs are increasing as well. To tackle this issue, we propose a lattice structure that autonomously learns the optimal combination patterns of network blocks, which enables the efficient and precise acquisition of feature representations, and ultimately achieves lightweight stereoSR. Specifically, we draw inspiration from the lattice phase equalizer and design lattice stereo NAFBlock (LSNB) to bridge pairs of NAFBlocks using re-weight block (RWBlock) through a coupled butterfly-style topological structures. RWBlock empowers LSNB with the capability to explore various combination patterns of pairwise NAFBlocks by adaptive re-weighting of feature. Moreover, we propose a lattice stereo attention module (LSAM) to search and transfer the most relevant features from another view. The resulting tightly interlinked architecture, named as LSSR, extensive experiments demonstrate that our method performs competitively to the state-of-the-art.

The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of the generated images. Exploring diffusion models to enhance the generated images nevertheless is not trivial and necessitates to delicately enrich plentiful details while preserving the visual appearance of key content in the original image. In this paper, we propose a novel framework, namely FreeEnhance, for content-consistent image enhancement using the off-the-shelf image diffusion models. Technically, FreeEnhance is a two-stage process that firstly adds random noise to the input image and then capitalizes on a pre-trained image diffusion model (i.e., Latent Diffusion Models) to denoise and enhance the image details. In the noising stage, FreeEnhance is devised to add lighter noise to the region with higher frequency to preserve the high-frequent patterns (e.g., edge, corner) in the original image. In the denoising stage, we present three target properties as constraints to regularize the predicted noise, enhancing images with high acutance and high visual quality. Extensive experiments conducted on the HPDv2 dataset demonstrate that our FreeEnhance outperforms the state-of-the-art image enhancement models in terms of quantitative metrics and human preference. More remarkably, FreeEnhance also shows higher human preference compared to the commercial image enhancement solution of Magnific AI.

The challenging task composed image retrieval targets at identifying the matched image from the multi-modal query with a reference image and a textual modifier. Most existing methods are devoted to composing the unified query representations from the query images and texts, yet the distribution gaps between the hybrid-modal query representations and visual target representations are neglected. However, directly incorporating target features on the query may cause ambiguous rankings and poor robustness due to the insufficient exploration of the distinguishments and overfitting issues. To address the above concerns, we propose a novel framework termed SemAntic Distillation from Neighborhood (SADN) for composed image retrieval. For mitigating the distribution divergences, we construct neighborhood sampling from the target domain for each query and aggregate neighborhood features with adaptive weights to restructure the query representations. Specifically, the adaptive weights are determined by the collaboration of two individual modules, as correspondence-induced adaption and divergence-based correction. Correspondence-induced adaption accounts for capturing the correlation alignments from neighbor features under the guidance of the positive representations, and the divergence-based correction regulates the weights based on the embedding distances between hard negatives and the query in the latent space. Extensive results and ablation studies on CIRR and FashionIQ validate that the proposed semantic distillation from neighborhood significantly outperforms baseline methods.

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing real speech. This task falls under the umbrella of articulatory-to-acoustic (A2A) conversion and may also be referred to as a silent speech interface. To overcome the domain discrepancy between silent and standard vocalized articulation, we introduce a novel pseudo target generation strategy. It integrates the text modality to align with articulatory movements, thereby guiding the generation of pseudo acoustic features for supervised training on speech reconstruction from silent articulation. Furthermore, we propose to employ a denoising diffusion probabilistic model as the fundamental architecture for the A2A conversion task and train the model using a combined training approach with the generated pseudo acoustic features. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in the silent speaking mode compared to all baseline methods. Specifically, the word error rate of the reconstructed speech decreases by approximately 5% when measured using an automatic speech recognition engine for intelligibility assessment, and the subjective mean opinion score for naturalness improves by 0.14. Moreover, analytical experiments reveal that the proposed pseudo target generation strategy can generate pseudo acoustic features that synchronize better with articulatory movements than previous strategies. Samples are available at our project page.

The Conversational Recommendation System (CRS) aims to capture user dynamic preferences and provide item recommendations based on multi-turn conversations. However, effectively modeling these dynamic preferences faces challenges due to conversational limitations, which mainly manifests as limited turns in a conversation (quantity aspect) and low compliance with queries (quality aspect). Previous studies often address these challenges in isolation, overlooking their interconnected nature. The fundamental issue underlying both problems lies in the potential abrupt changes in user preferences, to which CRS may not respond promptly. We acknowledge that user preferences are influenced by temporal factors, serving as a bridge between conversation quantity and quality. Therefore, we propose a more comprehensive CRS framework called Time-aware User-preference Tracking for Conversational Recommendation System (TUT4CRS), leveraging time dynamics to tackle both issues simultaneously. Specifically, we construct a global time interaction graph to incorporate rich external information and establish a local time-aware weight graph based on this information to adeptly select queries and effectively model user dynamic preferences. Extensive experiments on two real-world datasets validate that TUT4CRS can significantly improve recommendation performance while reducing the number of conversation turns.

Camouflaged instance segmentation (CIS) aims to detect and segment objects blending with their surroundings. While existing CIS methods rely heavily on fully-supervised training with massive precisely annotated data, consuming considerable annotation efforts yet struggling to segment highly camouflaged objects accurately. Despite their visual similarity to the background, camouflaged objects differ semantically. Since text associated with images offers explicit semantic cues to underscore this difference, we propose a novel approach: the first Text-Prompt based weakly-supervised camouflaged instance segmentation method named TPNet, leveraging semantic distinctions for effective segmentation. TPNet operates in two stages: pseudo mask generation and a self-training process. In the first stage, we align text prompts with images using a language-image model to obtain region proposals containing camouflaged instances. A Semantic-Spatial Iterative Fusion module is designed to assimilate spatial information with semantic insights, iteratively refining pseudo mask. In the second stage, Graduated Camouflage Learning, a self-training strategy, sequences training from simple to complex images based on camouflage levels, facilitating an effective learning gradient. Through the collaboration of the dual phases, our method offers a comprehensive experiment on two common benchmark and demonstrates a significant advancement, delivering a novel solution that bridges the gap between weak-supervised and high camouflaged instance segmentation.

For AI systems to be safely and reliably grounded in the real world, they should possess the ability of physical commonsense reasoning. Physical commonsense reasoning is essentially a multisensory task as physical properties of objects are manifested through multiple perception modalities, including both visual and auditory. In this study, we constructed two new benchmarks, called PACS-Reason and PACS-Reason+, for explainable physical audiovisual commonsense reasoning (EPACS), in which each datapoint is accompanied by a golden detailed rationale (intermediate reasoning path) to explain the answer selection. Moreover, we present PAVC-Reasoner, a multimodal large language model (LLM) designed to reason about physical commonsense attributes. The model aligns different modalities with the language modality by integrating three different perceivers for cross-modal pretraining and instruction finetuning at multiple granularities. It utilizes an LLM as a cognitive engine to process multimodal inputs and output convincing intermediate reasoning paths as justification for inferring answers. Numerous experiments have demonstrated the effectiveness and superiority of PAVC-Reasoner as a baseline model for studying EPACS. Most attractively, PAVC-Reasoner is capable of reasoning and obtaining strong interpretable explicit reasoning paths, signifying a significant stride towards real-world physical commonsense reasoning.

Single domain generalization (SDG) aims to learn a generalizable model from only one source domain available to unseen target domains. Existing SDG techniques rely on data or feature augmentation to generate distributions that complement the source domain. However, these approaches fail to address the challenge where gradient conflicts from synthesized domains impede the learning of domain-invariant representation. Inspired by the concept of mechanical equilibrium in physics, we propose a novel conflict-aware approach named domain gradient equilibrium for SDG. Unlike prior conflict-aware SDG methods that alleviate the gradient conflicts by setting them to zero or random values, the proposed domain gradient equilibrium method first decouples gradients into domaininvariant and domain-specific components. The domain-specific gradients are then adjusted and reweighted to achieve equilibrium, steering the model optimization toward a domain-invariant direction to enhance generalization capability. We conduct comprehensive experiments on four image recognition benchmarks, and our method achieves an accuracy improvement of 2.94% in the PACS dataset over existing state-of-the-art approaches, demonstrating the effectiveness of our proposed approach.

Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse-grained action categories, lacking fine-grained hand-action localization information. To fill this gap, we introduce the FHA-Kitchens (Fine-Grained Hand Actions in Kitchen Scenes) dataset, providing both coarse- and fine-grained hand action categories along with localization annotations. This dataset comprises 2,377 video clips and 30,047 frames, annotated with approximately 200k bounding boxes and 880 action categories. Evaluation of existing action detection methods on FHA-Kitchens reveals varying generalization capabilities across different granularities. To handle multi-granularity in hand actions, we propose MG-HAD, an End-to-End Multi-Granularity Hand Action Detection method. It incorporates two new designs: Multi-dimensional Action Queries and Coarse-Fine Contrastive Denoising. Extensive experiments demonstrate MG-HAD's effectiveness for multi-granularity hand action detection, highlighting the significance of FHA-Kitchens for future research and real-world applications. The dataset and source code are available at MG-HAD.

Diffusion models have achieved remarkable success in generating realistic images but suffer from generating accurate human hands, such as incorrect finger counts or irregular shapes. This difficulty arises from the complex task of learning the physical structure and pose of hands from training images, which involves extensive deformations and occlusions. For correct hand generation, our paper introduces a lightweight post-processing solution called HandRefiner. HandRefiner employs a conditional inpainting approach to rectify malformed hands while leaving other parts of the image untouched. We leverage the hand mesh reconstruction model that consistently adheres to the correct number of fingers and hand shape, while also being capable of fitting the desired hand pose in the generated image. Given a generated failed image due to malformed hands, we utilize ControlNet modules to re-inject such correct hand information. Additionally, we uncover a phase transition phenomenon within ControlNet as we vary the control strength. It enables us to take advantage of more readily available synthetic data without suffering from the domain gap between realistic and synthetic hands. Experiments demonstrate that HandRefiner can significantly improve the generation quality quantitatively and qualitatively. The code is available at https://github.com/wenquanlu/HandRefiner.

Training Single-Image Super-Resolution (SISR) models using pixel-based regression losses can achieve high distortion metrics scores (e.g., PSNR and SSIM), but often results in blurry images due to insufficient recovery of high-frequency details. Conversely, using GAN or perceptual losses can produce sharp images with high perceptual metric scores (e.g., LPIPS), but may introduce artifacts and incorrect textures. Balancing these two types of losses can help achieve a trade-off between distortion and perception, but the challenge lies in tuning the loss function weights. To address this issue, we propose a novel method that incorporates Multi-Objective Optimization (MOO) into the training process of SISR models to balance perceptual quality and distortion. We conceptualize the relationship between loss weights and image quality assessment (IQA) metrics as black-box objective functions to be optimized within our Multi-Objective Bayesian Optimization Super-Resolution (MOBOSR) framework. This approach automates the hyperparameter tuning process, reduces overall computational cost, and enables the use of numerous loss functions simultaneously. Extensive experiments demonstrate that MOBOSR outperforms state-of-the-art methods in terms of both perceptual quality and distortion, significantly advancing the perception-distortion Pareto frontier. Our work points towards a new direction for future research on balancing perceptual quality and fidelity in nearly all image restoration tasks. The source code and pretrained models are available at: https://github.com/ZhuKeven/MOBOSR.

The tasks of view synthesis and decoupling dynamic objects from the static environment for monocular scenes are both long-standing challenges in CV and CG. Most of the previous NeRF-based methods rely on implicit representation, which require additional supervision and training time. Later, various explicit representations like multi-planes or 3D gaussian splatting have been extended and applied to the task of novel view synthesis for dynamic scenes. They introduce an additional time dimension or a deformation field into the original representation to encode dynamics. Due to the effective explicit representations, these methods greatly reduce the time consumption, but still fail to achieve high rendering quality in some scenes, especially for some real scenes. For the latter decoupling problem, previous neural radiation field methods require frequent tuning of the relevant parameters for different scenes, which is very inconvenient for practical use. We consider above problems and propose a new representation of dynamic scenes based on tensor decomposition, which we call R4D-planes. The key to our method is remapping, which compensates for the shortcomings of the plane structure by fusing space-time information and remapping to new indexes. Furthermore, we implement a new decoupling structure, which can efficiently decouple dynamic and static scenes in a self-supervised manner. Experimental results show our method achieves better rendering quality and training efficiency in both view synthesis and decoupling tasks for monocular scenes.

Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of parameters struggle to handle such tasks satisfactorily. To address this, we propose OneChart: a reliable agent specifically devised for the structural extraction of chart information. Similar to popular LVLMs, OneChart incorporates an autoregressive main body. Uniquely, to enhance the reliability of the numerical parts of the output, we introduce an auxiliary token placed at the beginning of the total tokens along with an additional decoder. The numerically optimized (auxiliary) token allows subsequent tokens for chart parsing to capture enhanced numerical features through causal attention. Furthermore, with the aid of the auxiliary token, we have devised a self-evaluation mechanism that enables the model to gauge the reliability of its chart parsing results by providing confidence scores for the generated content. Compared to current state-of-the-art (SOTA) chart parsing models, e.g., DePlot, ChartVLM, ChartAst, OneChart significantly outperforms in Average Precision (AP) for chart structural extraction across multiple public benchmarks, despite enjoying only 0.2 billion parameters. Moreover, as a chart parsing agent, it also brings 10%+ accuracy gains for the popular LVLM (LLaVA-1.6) in the downstream ChartQA benchmark.

Weakly-supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduces WeakSAM and solves the weakly-supervised object detection (WSOD) and segmentation by utilizing the pre-learned world knowledge contained in a vision foundation model, i.e., the Segment Anything Model (SAM). WeakSAM addresses two critical limitations in traditional WSOD retraining, i.e., pseudo ground truth (PGT) incompleteness and noisy PGT instances, through adaptive PGT generation and Region of Interest (RoI) drop regularization. It also addresses the SAM's shortcomings of requiring human prompts and category unawareness in object detection and segmentation. Our results indicate that WeakSAM significantly surpasses previous state-of-the-art methods in WSOD and WSIS benchmarks with large margins, i.e. average improvements of 7.4% and 8.5%, respectively.

As one of the crucial elements in human-robot interaction, responsive listening head generation has attracted considerable attention from researchers. It aims to generate a listening head video based on speaker's audio and video as well as a reference listener image. However, existing methods exhibit two limitations: 1) the generation capability of their models is limited, resulting in generated videos that are far from real ones, and 2) they mostly employ autoregressive generative models, unable to mitigate the risk of error accumulation. To tackle these issues, we propose Listenformer that leverages the powerful temporal modeling capability of transformers for generation. It can perform non-autoregressive prediction with the proposed two-stage training method, simultaneously achieving temporal continuity and overall consistency in the outputs. To fully utilize the information from the speaker inputs, we designed an audio-motion attention fusion module, which improves the correlation of audio and motion features for accurate response. Additionally, a novel decoding method called sliding window with a large shift is proposed for Listenformer, demonstrating both excellent computational efficiency and effectiveness. Extensive experiments show that Listenformer outperforms the existing state-of-the-art methods on ViCo and L2L datasets. And a perceptual user study demonstrates the comprehensive performance of our method in generating diversity, identity preserving, speaker-listener synchronization, and attitude matching. Our code is available at https://liushenme.github.io/ListenFormer.github.io/.

The neural radiance field (NeRF) has made significant strides in representing 3D scenes and synthesizing novel views. Despite its advancements, the high computational costs of NeRF have posed challenges for its deployment in resource-constrained environments and real-time applications. As an alternative to NeRF-like neural rendering methods, 3D Gaussian Splatting (3DGS) offers rapid rendering speeds while maintaining excellent image quality. However, as it represents objects and scenes using a myriad of Gaussians, it requires substantial storage to achieve high-quality representation. To mitigate the storage overhead, we propose Factorized 3D Gaussian Splatting (F-3DGS), a novel approach that drastically reduces storage requirements while preserving image quality. Inspired by classical matrix and tensor factorization techniques, our method represents and approximates dense clusters of Gaussians with significantly fewer Gaussians through efficient factorization. We aim to efficiently represent dense 3D Gaussians by approximating them with a limited amount of information for each axis and their combinations. This method allows us to encode a substantially large number of Gaussians along with their essential attributes'such as color, scale, and rotation-necessary for rendering using a relatively small number of elements. Extensive experimental results demonstrate that F-3DGS achieves a significant reduction in storage costs while maintaining comparable quality in rendered images. Our project page is available at https://xiangyu1sun.github.io/Factorize-3DGS/.

Due to the limitation of collection device and unstable scanning process, point cloud data is usually noisy. This noise deforms the underlying structures of point clouds and inevitably affects downstream tasks such as rendering, reconstruction and classification. In this paper, we propose a Cross-stage Cross-coder Adaptive Edge Graph Convolution Network (C2AENet) to denoise point clouds. Our network uses multiple stages to progressively and iteratively denoise points. To improve the effectiveness, we add connections between two stages and between the encoder and decoder, leading to the cross-stage cross-coder architecture. Additionally, existing graph-based point cloud learning methods tend to capture the local structure. They typically construct a semantic graph based on semantic distance, which may ignore Euclidean neighbors and lead to insufficient geometry perception. Therefore, we introduce a geometric graph and adaptively calculate edge attention based on the local and global structural information of the points. This results in a novel graph convolution module that allows the network to capture richer contextual information and focus on more important parts. Extensive experiments demonstrate that the proposed method is competitive compared with other state-of-the-art methods. The code is available at: https://github.com/chenwuwq/C2AENet.

Foundational segmentation models, predominantly trained on scenes typical of natural environments, struggle to generalize across varied image domains. Traditional "training-to-adapt'' methods rely heavily on extensive data retraining and model architectures modifications. This significantly limits the models' generalization capabilities and efficiency in deployment. In this study, we propose a novel adaptation paradigm, termed "prompting-to-adapt'', to tackle the above issue by introducing an innovative image prompter. This prompter generates domain-specific prompts through few-shot image-mask pairs, incorporating diverse image processing techniques to enhance adaptability. To tackle the inherent non-differentiability of image prompts, we further devise an information-estimation-based gradient descent strategy that leverages the information entropy of image processing combinations to optimize the prompter, ensuring effective adaptation. Through extensive experiments across nine datasets spanning seven image domains (i.e., depth, thermal, camouflage, endoscopic, ultrasound, grayscale, and natural) and four scenarios (i.e., common scenes, camouflage objects, medical images, and industrial data), we demonstrate that our approach significant improves the foundational models' adaptation capabilities. Moreover, the interpretability of the generated prompts provides insightful revelations into their image processing mechanisms. Source code is available at: \urlgithub.com/yuema1303/Prompting-to-Adapt-FSM.