ACM MM 2024 论文列表

Amidst the prevailing trend of escalating demands for data and computational resources, the efficiency of data utilization emerges as a critical lever for enhancing the performance of deep learning models, especially in the realm of image restoration tasks. This investigation delves into the intricacies of data efficiency in the context of image restoration, with Gaussian image denoising serving as a case study. We postulate a strong correlation between the model's performance and the content information encapsulated in the training images. This hypothesis is rigorously tested through experiments conducted on synthetically blurred datasets. Building on this premise, we delve into the data efficiency within training datasets and introduce an effective and stabilized method for quantifying content information, thereby enabling the ranking of training images based on their influence. Our in-depth analysis sheds light on the impact of various subset selection strategies, informed by this ranking, on model performance. Furthermore, we examine the transferability of these efficient subsets across disparate network architectures. The findings underscore the potential to achieve comparable, if not superior, performance with a fraction of the data-highlighting instances where training IRCNN and Restormer models with only 3.89% and 2.30% of the data resulted in a negligible drop and, in some cases, a slight improvement in PSNR. This investigation offers valuable insights and methodologies to address data efficiency challenges in Gaussian denoising. Similarly, our method yields comparable conclusions in other restoration tasks. We believe this will be beneficial for future research.

Generating photorealistic animations from a single still photo represents a significant advancement in multimedia editing and artistic creation. While existing AIGC methods have reached milestone successes, they often struggle with maintaining consistency with real-world physical laws, particularly in fluid dynamics. To address this issue, this paper introduces ANFluid, a physics solver and data-driven coupled framework that combines physics-aware simulation (PAS) and dual-flow texture learning (DFTL) to animate natural fluid photos effectively. The PAS component of ANFluid ensures that motion guides adhere to physical laws, and can be automatically tailored with specific numerical solver to meet the diversities of different fluid scenes. Concurrently, DFTL focuses on enhancing texture prediction. It employs bidirectional self-supervised optical flow estimation and multi-scale wrapping to strengthen dynamic relationships and elevate the overall animation quality. Notably, despite being built on a transformer architecture, the innovative encoder-decoder design in DFTL does not increase the parameter count but rather enhances inference efficiency. Extensive quantitative experiments have shown that our ANFluid surpasses most current methods on the Holynski and CLAW datasets. User studies further confirm that animations produced by ANFluid maintain better physical and content consistency with the real world and the original input, respectively. Moreover, ANFluid supports interactive editing during the simulation process, enriching the animation content and broadening its application potential.

The existing methods for Vision and Language Navigation in the Continuous Environment (VLN-CE) commonly incorporate a waypoint predictor to discretize the environment. This simplifies the navigation actions into a view selection task and improves navigation performance significantly compared to direct training using low-level actions. However, the VLN-CE agents are still far from the real robots since there are gaps between their visual perception and executed actions. First, VLN-CE agents that discretize the visual environment are primarily trained with high-level view selection, which causes them to ignore crucial spatial reasoning within the low-level action movements. Second, in these models, the existing waypoint predictors neglect object semantics and their attributes related to passibility, which can be informative in indicating the feasibility of actions. To address these two issues, we introduce a low-level action decoder jointly trained with high-level action prediction, enabling the current VLN agent to learn and ground the selected visual view to the low-level controls. Moreover, we enhance the current waypoint predictor by utilizing visual representations containing rich semantic information and explicitly masking obstacles based on humans' prior knowledge about the feasibility of actions. Empirically, our agent can improve navigation performance metrics compared to the strong baselines on both high-level and low-level actions.

In this paper, we introduce a new challenging task called Zero-Shot Controllable Image-to-Video Animation, where the goal is to animate an image based on motion trajectories defined by the user, without fine-tuning the base model. Primary challenges include maintaining consistency of background, consistency of object in motion, faithfulness to the user-defined trajectory, and quality of motion animation. We also introduce a novel approach for this task, leveraging diffusion models called Img2VidAnim-Zero (IVA0). IVA0 tackles our controllable Image-to-Video (I2V) task by decomposing it into two subtasks: 'out-of-place' and 'in-place' motion animation. Due to this decomposition, IVA0 can leverage existing work on layout-conditioned image generation for out-of-place motion generation, and existing text-conditioned video generation methods for in-place motion animation, thus facilitating zero-shot generation. Our model also addresses key challenges for controllable animation, such as Layout Conditioning via Spatio-Temporal Masking to incorporate user guidance and Motion Afterimage Suppression (MAS) scheme to reduce object ghosting during out-of-place animation. Finally, we design a novel controllable I2V benchmark featuring diverse local- and global-level metrics. Results show IVA0 as a new state-of-the-art, establishing a new standard for the zero-shot controllable I2V task. Our method highlights the simplicity and effectiveness of task decomposition and modularization for this novel task for future studies. Our code and visualizations are available at https://img2vidanim-0.github.io/

Image captioning evaluation metrics can be divided into two categories, reference-based metrics and reference-free metrics. However, reference-based approaches may struggle to evaluate descriptive captions with abundant visual details produced by advanced multimodal large language models, due to their heavy reliance on limited human-annotated references. In contrast, previous reference-free metrics have been proven effective via CLIP cross-modality similarity. Nonetheless, CLIP-based metrics, constrained by their solution of global image-text compatibility, often have a deficiency in detecting local textual hallucinations and are insensitive to small visual objects. Besides, their single-scale designs are unable to provide an interpretable evaluation process such as pinpointing the position of caption mistakes and identifying visual regions that have not been described. To move forward, we propose a novel reference-free metric for image captioning evaluation, dubbed Hierarchical Image Captioning Evaluation Score (HICE-S). By detecting local visual regions and textual phrases, HICE-S builds an interpretable hierarchical scoring mechanism, breaking through the barriers of the single-scale structure of existing reference-free metrics. Comprehensive experiments indicate that our proposed metric achieves the SOTA performance on several benchmarks, outperforming existing reference-free metrics like CLIP-S and PAC-S, and reference-based metrics like METEOR and CIDEr. Moreover, several case studies reveal that the assessment process of HICE-S on detailed captions closely resembles interpretable human judgments.Our code is available at https://github.com/joeyz0z/HICE.

Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which the model itself (the in-training model) has high confidence, e.g., `small loss', can suffer from the so called `self-confirmation' bias. This bias arises because the in-training model, is at least partially trained on the noisy labels. Furthermore, in the classification case, an additional challenge arises because some of the label noise is between classes that are visually very similar (`hard noise'). This paper addresses these challenges by proposing a method (\textit{CLIPCleaner}) that leverages CLIP, a powerful Vision-Language (VL) model for constructing a zero-shot classifier for efficient, offline, clean sample selection. This has the advantage that the sample selection is decoupled from the in-training model and that the sample selection is aware of the semantic and visual similarities between the classes due to the way that CLIP is trained. We provide theoretical justifications and empirical evidence to demonstrate the advantages of CLIP for LNL compared to conventional pre-trained models. Compared to current methods that combine iterative sample selection with various techniques, \textit{CLIPCleaner} offers a simple, single-step approach that achieves competitive or superior performance on benchmark datasets. To the best of our knowledge, this is the first time a VL model has been used for sample selection to address the problem of Learning with Noisy Labels (LNL), highlighting their potential in the domain.

Cross-area evaluation poses a significant challenge for ground-to-aerial geo-localization, in which the training and testing data are captured from entirely distinct areas. However, current methods struggle in cross-area evaluation due to their emphasis solely on learning global information from single-scale features. Some efforts alleviate this problem but rely on complex and specific technologies like pre-processing and hard sample mining. To this end, we propose a pure end-to-end solution, free from task-specific techniques, termed the Multi-scale Feature Representation Generalization Network (MFRGN) to improve generalization. Specifically, we introduce multi-scale features and explicitly utilize them by an novel global-local information representation structure with two flows, to bolster feature representations. In the global flow, we present a lightweight Self and Cross Attention Module (SCAM) to efficiently learn global embeddings. In the local flow, we develop a Global-Prompt Attention Block (GPAB) to capture discriminative features under the global embeddings as prompts. As a result, our approach generates robust descriptors representing multi-scale global and local information, thereby enhancing the model's invariance to scene variations. Extensive experiments on benchmarks show our MFRGN achieves competitive performance in same-area evaluation and improves cross-area generalization by a significant margin compared to SOTA methods. Our code is available at https://github.com/ytao-wang/MFRGN.

The technique of 3D Gaussian splatting (3DGS) has demonstrated its effectiveness and efficiency in rendering photo-realistic images for novel view synthesis. However, 3DGS requires a high density of camera coverage, and its performance inevitably degrades with sparse training views, which significantly limits its applicability in real-world scenarios. In recent years, many researchers have explored the use of depth information to alleviate this problem, but the performance of their methods is sensitive to the accuracy of depth estimation. To this end, we propose an efficient method to enhance the performance of 3DGS with sparse training views. Specifically, instead of applying depth maps for regularization, we propose a densification method that generates high-quality point clouds, providing a superior initialization for 3D Gaussians. Furthermore, we propose Systematically Angle of View Sampling (SAOVS), which employs Spherical Linear Interpolation (SLERP) and linear interpolation for side view sampling, to determine unseen views outside the training data for semantic pseudo-label regularization. Experiments show that our proposed method significantly outperforms other leading 3D rendering models on the ScanNet dataset and the LLFF dataset. In particular, compared with the conventional 3DGS method, our proposed method achieves performance gains of up to 1.71dB in PSNR and 0.07 in SSIM. In addition, the novel view synthesis produced by our method demonstrates the highest visual quality with minimal distortions.

Recently, Segment Anything Model (SAM) has become a research hotspot in the fields of multimedia and computer vision, which exhibits powerful yet versatile capabilities on various (un) conditional image segmentation tasks. Although SAM can support different types of segmentation prompts, we note that, compared to point- and box-guided segmentations, it performs much worse on text-instructed tasks, e.g., referring image segmentation (RIS). In this paper, we argue that deep text instruction tuning is key to mitigate such shortcoming caused by the shallow fusion scheme in its default light-weight mask decoder. To address this issue, we propose two simple yet effective deep instruction tuning (DIT) methods for SAM, one is end-to-end and the other is layer-wise. With minimal modifications, DITs can directly transform the image encoder of SAM as a stand-alone vision-language learner in contrast to building another deep fusion branch, maximizing the benefit of its superior segmentation capability. Extensive experiments on three highly competitive benchmark datasets of RIS show that a simple end-to-end DIT can improve SAM by a large margin, while the layer-wise DIT can further boost the performance to state-of-the-art with much less data and training expenditures. Our code is released at: https://github.com/wysnzzzz/DIT.

In this paper, we propose a neural representation for videos that enables real-time quality-scalable decoding, called QS-NeRV. QS-NeRV comprises a Self-Learning Distribution Mapping Network (SDMN) and Extensible Enhancement Networks (EENs). Firstly, SDMN functions as the base layer (BL) for scalable video coding, focusing on encoding videos of lower quality. Within SDMN, we employ a methodology that minimizes the bitstream overhead to achieve efficient information exchange between the encoder and decoder instead of direct transmission. Specifically, we utilize an invertible network to map the multi-scale information obtained from the encoder to a specific distribution. Subsequently, during the decoding process, this information is recovered from a randomly sampled latent variable to assist the decoder in achieving improved reconstruction performance. Secondly, EENs serve as the enhancement layers (ELs) and are trained in an overfitting manner to obtain robust restoration capability. By integrating the fixed BL bitstream with the parameters of EEN as an extension pack, the decoder can produce higher-quality enhanced videos. Furthermore, the scalability of the method allows for adjusting the number of combined packs to accommodate diverse quality requirements. Experimental results demonstrate our proposed QS-NeRV outperforms the state-of-the-art real-time decoding INR-based methods on various datasets for video compression and interpolation tasks.

Conversation is a common form of human communication that includes extensive emotional interaction. Traditional approaches focused on studying emotions and their underlying causes in conversations. They try to address two issues: what emotions are present in the dialogue and what causes these emotions. However, these works often overlook the bidirectional nature of emotional interaction in dialogue: utterances can evoke emotions (cause), and emotions can also lead to certain utterances (consequence). Therefore, we propose a new issue: what consequences arise from these emotions? This leads to the introduction of a new task called Emotion Consequence Forecasting in CONversations (ECFCON). In this work, we first propose a corresponding dialogue-level dataset. Specifically, we select 2,780 video dialogues for annotation, totaling 39,950 utterances. Out of these, 12,391 utterances contain emotions, and 8,810 of these have discernible consequences. Then, we benchmark this task by conducting experiments from the perspectives of traditional methods, generalized LLMs prompting methods, and clue-driven hybrid methods. Both our dataset and benchmark codes are openly accessible to the public.

Recent developments in the field of diffusion models have demonstrated an exceptional capacity to generate high-quality prompt-conditioned image edits. Nevertheless, previous approaches have primarily relied on textual prompts for image editing, which tend to be less effective when making precise edits to specific objects or fine-grained regions within a scene containing single/multiple objects. We introduce a novel framework for zero-shot localized multi-object editing through a multi-diffusion process to overcome this challenge. This framework empowers users to perform various operations on objects within an image, such as adding, replacing, or editing $\textbf{many}$ objects in a complex scene $\textbf{in one pass}$. Our approach leverages foreground masks and corresponding simple text prompts that exert localized influences on the target regions resulting in high-fidelity image editing. A combination of cross-attention and background preservation losses within the latent space ensures that the characteristics of the object being edited are preserved while simultaneously achieving a high-quality, seamless reconstruction of the background with fewer artifacts compared to the current methods. We also curate and release a dataset dedicated to multi-object editing, named $\texttt{LoMOE}$-Bench. Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.

Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS) transformation or appearance flow to deform in-shop clothing for aligning with the person's body. Despite their promising performance, these methods often lack precise control over fine details, leading to inconsistencies in shape between clothing and the person's body as well as distortions in exposed limb regions. To tackle these challenges, we propose a novel shape-guided clothing warping method for virtual try-on, dubbed SCW-VTON, which incorporates global shape constraints and additional limb textures to enhance the realism and consistency of the warped clothing and try-on results. To integrate global shape constraints for clothing warping, we devise a dual-path clothing warping module comprising a shape path and a flow path. The former path captures the clothing shape aligned with the person's body, while the latter path leverages the mapping between the pre- and post-deformation of the clothing shape to guide the estimation of appearance flow. Furthermore, to alleviate distortions in limb regions of try-on results, we integrate detailed limb guidance by developing a limb reconstruction network based on masked image modeling. Through the utilization of SCW-VTON, we are able to generate try-on results with enhanced clothing shape consistency and precise control over details. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods both qualitatively and quantitatively.

Document Visual Question Answering (DVQA) involves responding to queries based on the contents of document images. Existing works are confined to locating information within a single page and lack support for cross-page question-and-answer interactions. Furthermore, the token length limitation on model inputs can lead to the truncation of answer-relevant segments. In this study, we present CREAM, an innovative methodology that focuses on high-performance retrieval and integrates relevant multimodal document information to effectively address this critical issue. To overcome the limitations of current text embedding similarity methods, we first employ a coarse-to-fine retrieval and ranking approach. The coarse phase calculates the similarity between the query and text chunk embeddings, while the fine phase involves multiple rounds of grouping and ordering with a large language model to identify the text chunks most relevant to the query. Subsequently, integrating an attention pooling mechanism for multi-page document images into the vision encoder allows us to effectively merge the visual information of multi-page documents, enabling the multimodal large language model (MLLM) to simultaneously process both single-page and multi-page documents. Finally, we apply various parameter-efficient tuning methods to enhance document visual question-answering performance. Experiments demonstrate that our approach secures state-of-the-art results across various document datasets.

The fusion of visible and infrared images aims to produce high-quality fusion images with rich textures and salient target information. Existing methods lack interactivity and flexibility in the execution of fusion. It is unfeasible to express the requirements to modify the fusion effect, and the different regions in the source images are treated equally across the identical fusion model, which causes fusion homogenization and low distinction. Besides, their pre-defined fusion strategies invariably lead to monotonous effects, which are insufficiently comprehensive. They fail to adequately consider data credibility, scene illumination, and noise degradation inherent in the source information. To address these issues, we propose the Te xt-driven and Region-aware Flexible visible and infrared image fusion, termed as TeRF. On the one hand, we propose a flexible image fusion framework with multiple large language and vision models, which facilitates the visual-text interaction. On the other hand, we aggregate comprehensive fine-tuning paradigms for the different fusion requirements to build a unified fine-tuning pipeline. It allows the linguistic selection of the regions and effects, yielding visually appealing fusion outcomes. Extensive experiments demonstrate the competitiveness of our method both qualitatively and quantitatively compared to existing state-of-the-art methods. Our code is publicly available at https://github.com/Baixuzx7/TeRF.

We design ScaleTraversal, an interactive tool for creating multi-scale 3D demonstration animations with limited resources for users who are unavailable to access high performance machines such as clusters or super computers. It is difficult to create 3D demonstration animations for multi-scale data. First, it is challenging to strike a balance between flexibility and user friendliness to design the user interface in customizing demonstration animations. Second, the multi-scale biomedical data is often characterized as large-size so that it is hard for users to handle it by a desktop PC. We design an interactive bi-functional user interface to create multi-scale biomedical demonstration animations intuitively. It fully utilizes the strengths of graphical interface's user friendliness and textual interface's flexibility, which enables users to customize demonstration animations from macro-scales to meso- and micro-scales. Furthermore, we design three scale-based memory management strategies to solve the issues presented in multi-scale data, including a streaming data processing strategy, a scale-based data prefetching strategy and a GPU acceleration strategy for rendering. Finally, we conduct both quantitative evaluation and qualitative evaluation to demonstrate the efficiency, expressiveness and usability of ScaleTraversal.

Scene Graph Generation (SGG) is an important cross-modal task in scene understanding, aiming to detect visual relations in an image. However, due to the various appearance features, the feature distributions of different categories have suffered from a severe overlap, which makes the decision boundaries ambiguous. The current SGG methods mainly attempt to re-balance the data distribution, which is dataset-dependent and limits the generalization. To solve this problem, a Synergetic Prototype Learning Network (SPLN) is proposed here, where the generalized semantic space is modeled and the synergetic effect among different semantic subspaces is delved into. In SPLN, a Collaboration-induced Prototype Learning method is proposed to model the interaction of visual semantics and structural semantics. The conventional visual semantics is focused on with a residual-driven representation enhancement module to capture details. And the intersection of structural semantics and visual semantics is explicitly modeled as conceptual semantics, which has been ignored by existing methods. Meanwhile, to alleviate the noise of unrelated and meaningless words, an Intersection-induced Prototype Learning method is also proposed specially for conceptual semantics with an essence-driven prototype enhancement module. Moreover, a Selective Fusion Module is proposed to synergetically integrate the results of visual, structural, conceptual branches and the generalized semantics projection. Experiments on VG and GQA datasets show that our method achieves state-of-the-art performance on the unbiased metrics.