Automated diagnosis of depression is crucial for early detection and timely intervention. Previous research has largely concentrated on visual information, often neglecting the value of leveraging a variety of data types. Although some studies have attempted to employ multiple modalities, they typically fall short in investigating the complex dynamics between features from various modalities over time. To address this challenge, we present an innovative Multi-modal Dual-Attention aggregation architecture for Depression Recognition (MDDR). This framework leverages multi-modal pre-trained features and introduces two attention aggregation mechanisms: the Feature Alignment and Aggregation (FAA) module and the Sequence Encoding and Aggregation (SEA) module. The FAA module is designed to dynamically evaluate the relevance of multi-modal features for each instance, facilitating a dynamic integration of these features over time. Following this, the SEA module determines the importance of the amalgamated features for each frame, ensuring that aggregation is conducted based on their significance, to extract the most relevant features for accurately diagnosing depression. Moreover, we propose a unique loss calculation method specifically designed for depression assessment, named DR Loss. Our approach, evaluated on the AVEC2013 and AVEC2014 depression audiovisual datasets, achieves unparalleled performance.
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Depression is a prevalent mental health disorder that significantly impacts individuals' lives and well-being. Early detection and intervention are crucial for effective treatment and management of depression. Recently, there are many end-to-end deep learning methods leveraging the facial expression features for automatic depression detection. However, most current methods overlook the temporal dynamics of facial expressions. Although very recent 3DCNN methods remedy this gap, they introduce more computational cost due to the selection of CNN-based backbones and redundant facial features. To address the above limitations, by considering the timing correlation of facial expressions, we propose a novel framework called FacialPulse, which recognizes depression with high accuracy and speed. By harnessing the bidirectional nature and proficiently addressing long-term dependencies, the Facial Motion Modeling Module (FMMM) is designed in FacialPulse to fully capture temporal features. Since the proposed FMMM has parallel processing capabilities and has the gate mechanism to mitigate gradient vanishing, this module can also significantly boost the training speed. Besides, to effectively use facial landmarks to replace original images to decrease information redundancy, a Facial Landmark Calibration Module (FLCM) is designed to eliminate facial landmark errors to further improve recognition accuracy. Extensive experiments on the AVEC2014 dataset and MMDA dataset (a depression dataset) demonstrate the superiority of FacialPulse on recognition accuracy and speed, with the average MAE (Mean Absolute Error) decreased by 21% compared to baselines, and the recognition speed increased by 100% compared to state-of-the-art methods. Codes are released at https://github.com/volatileee/FacialPulse.
In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limited environments such as remote medical facilities and mobile devices. This necessitates effective dataset compression techniques to reduce storage, transmission, and computational cost. However, existing coreset selection methods are primarily designed for natural image datasets, and exhibit doubtful effectiveness when applied to medical image datasets due to challenges such as intra-class variation and inter-class similarity. In this paper, we propose a novel coreset selection strategy termed as Evolution-aware VAriance (EVA), which captures the evolutionary process of model training through a dual-window approach and reflects the fluctuation of sample importance more precisely through variance measurement. Extensive experiments on medical image datasets demonstrate the effectiveness of our strategy over previous SOTA methods, especially at high compression rates. EVA achieves 98.27% accuracy with only 10% training data, compared to 97.20% for the full training set. None of the compared baseline methods can exceed Random at 5% selection rate, while EVA outperforms Random by 5.61%, showcasing its potential for efficient medical image analysis.
This paper makes the first attempt towards unsupervised preference alignment in Vision-Language Models (VLMs). We generate chosen and rejected responses with regard to the original and augmented image pairs, and conduct preference alignment with direct preference optimization. It is based on a core idea: properly designed augmentation to the image input will induce VLM to generate false but hard negative responses, which helps the model to learn from and produce more robust and powerful answers. The whole pipeline no longer hinges on supervision from GPT-4 or human involvement during alignment, and is highly efficient with few lines of code. With only 8k randomly sampled unsupervised data, it achieves 90% relative score to GPT-4 on complex reasoning in LLaVA-Bench, and improves LLaVA-7B/13B by 6.7%/5.6% score on complex multi-modal benchmark MM-Vet. Visualizations shows its improved ability to align with user-intentions. A series of ablations are firmly conducted to reveal the latent mechanism of the approach, which also indicates its potential towards further scaling.
While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generative model learning has emerged as a solution to train private generative models for different downstream tasks. However, existing private generative modeling approaches face significant challenges in generating high-dimensional data due to the inherent complexity involved in modeling such data. In this work, we present a new private generative modeling approach where samples are generated via Hamiltonian dynamics with gradients of the private dataset estimated by a well-trained network. In the approach, we achieve differential privacy by perturbing the projection vectors in the estimation of gradients with sliced score matching. In addition, we enhance the reconstruction ability of the model by incorporating a residual enhancement module during the score matching. For sampling, we perform Hamiltonian dynamics with gradients estimated by the well-trained network, allowing the sampled data close to the private dataset's manifold step by step. In this way, our model is able to generate data with a resolution of 256×256. Extensive experiments and analysis clearly demonstrate the effectiveness and rationality of the proposed approach.
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
PDF ↗Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and diversity under scene constraints, they tend to overlook the fine-grained hand-object interactions such as contacts, resulting in inaccurate and undesired grasps. To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM). Particularly when synthesizing interactive grasps, the method enables the precise control of grasp synthesis through either user-specified or algorithmically predicted Semantic Contact Map. Specifically, to optimally utilize contact supervision constraints and to accurately model the complex physical structure of hands, we propose a Dual Generation Framework. Within this framework, the Semantic Conditional Module generates reasonable contact maps based on fine-grained contact information, while the Contact Conditional Module utilizes contact maps alongside object point clouds to generate realistic grasps. We evaluate the evaluation criteria applicable to controllable grasp generation. Both unimanual and bimanual generation experiments on GRAB and ARCTIC datasets verify the validity of our proposed method, demonstrating the efficacy and robustness of ClickDiff, even with previously unseen objects. Our code is available at https://github.com/adventurer-w/ClickDiff.
Advances in multimedia technology and its widespread application in education have made multimedia learning increasingly important. Knowledge Tracing (KT) is the key technology for achieving adaptive multimedia learning, aiming to monitor the degree of knowledge acquisition and predict students' performance during the learning process. Current KT research is dedicated to enhancing the performance of KT problems by integrating the most advanced deep learning techniques. However, this has led to increasingly complex models, which reduce model usability and divert researchers' attention away from exploring the core issues of KT. This paper aims to tackle the fundamental challenges of KT tasks, including the knowledge state representation and the core architecture design, and investigate a novel KT model that is both simple and powerful. We have revisited the KT task and propose the ReKT model. First, taking inspiration from the decision-making process of human teachers, we model the knowledge state of students from three distinct perspectives: questions, concepts, and domains. Second, building upon human cognitive development models, such as constructivism, we have designed a Forget-Response-Update (FRU) framework to serve as the core architecture for the KT task. The FRU is composed of just two linear regression units, making it an extremely lightweight framework. Extensive comparisons were conducted with 22 state-of-the-art KT models on 7 publicly available datasets. The experimental results demonstrate that ReKT outperforms all the comparative methods in question-based KT tasks, and consistently achieves the best (in most cases) or near-best performance in concept-based KT tasks. Furthermore, in comparison to other KT core architectures like Transformers or LSTMs, the FRU achieves superior prediction performance with approximately only 38% computing resources. Through an exploration of the ReKT model that is both simple and powerful, is able to offer new insights to future KT research. The code can be found at https://github.com/lilstrawberry/ReKT.
We present TimeNeRF, a generalizable neural rendering approach for rendering novel views at arbitrary viewpoints and at arbitrary times, even with few input views. For real-world applications, it is expensive to collect multiple views and inefficient to re-optimize for unseen scenes. Moreover, as the digital realm, particularly the metaverse, strives for increasingly immersive experiences, the ability to model 3D environments that naturally transition between day and night becomes paramount. While current techniques based on Neural Radiance Fields (NeRF) have shown remarkable proficiency in synthesizing novel views, the exploration of NeRF's potential for temporal 3D scene modeling remains limited, with no dedicated datasets available for this purpose. To this end, our approach harnesses the strengths of multi-view stereo, neural radiance fields, and disentanglement strategies across diverse datasets. This equips our model with the capability for generalizability in a few-shot setting, allows us to construct an implicit content radiance field for scene representation, and further enables the building of neural radiance fields at any arbitrary time. Finally, we synthesize novel views of that time via volume rendering. Experiments show that TimeNeRF can render novel views in a few-shot setting without per-scene optimization. Most notably, it excels in creating realistic novel views that transition smoothly across different times, adeptly capturing intricate natural scene changes from dawn to dusk.
Complaints are pivotal expressions within e-commerce communication, yet the intricate nuances of human interaction present formidable challenges for AI agents to grasp comprehensively. While recent attention has been drawn to analyzing complaints within a multimodal context, relying solely on text and images is insufficient for organizations. The true value lies in the ability to pinpoint complaints within the intricate structures of discourse, scrutinizing them at a granular aspect level. Our research delves into the discourse structure of e-commerce video-based product reviews, pioneering a novel task we term Aspect-Level Complaint Detection from Discourse (ACDD). Embedded in a multimodal framework, this task entails identifying aspect categories and assigning complaint/non-complaint labels at a nuanced aspect level. To facilitate this endeavour, we have curated a unique multimodal product review dataset, meticulously annotated at the utterance level with aspect categories and associated complaint labels. To support this undertaking, we introduce a Multimodal Aspect-Aware Complaint Analysis (MAACA) model that incorporates a novel pre-training strategy and a global feature fusion technique across the three modalities. Additionally, the proposed framework leverages a moment retrieval step to identify the relevant portion of the clip, crucial for accurately detecting the fine-grained aspect categories and conducting aspect-level complaint detection. Extensive experiments conducted on the proposed dataset showcase that our framework outperforms unimodal and bimodal baselines, offering valuable insights into the application of video-audio-text representation learning frameworks for downstream tasks. The dataset and code are available at: https://github.com/rdev12/MAACA.
Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencies, overlooking critical structural information integral to KGs. In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning. We first discuss on the existing LLM paradigms like in-context learning and instruction tuning, proposing basic structural information injection approaches. Then we propose a Knowledge Prefix Adapter (KoPA) to fulfill this stated goal. KoPA uses a structural pre-training phase to comprehend the intricate entities and relations within KGs, representing them as structural embeddings. Then KoPA communicates such cross-modal structural information understanding to the LLMs through a knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens positioned as a prefix of the input prompt. We conduct comprehensive experiments and provide incisive analysis. Our code and data are available at https://github.com/zjukg/KoPA.
Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The effect of the design of each component is still unclear. In addition, the iterative denoising process consumes considerable computational overhead, which is prohibitive for real-time scenarios such as virtual characters and humanoid robots. For this reason, we first conduct a comprehensive investigation into network architectures, training strategies, and inference process. Based on the profound analysis, we tailor each component for efficient high-quality human motion generation. Despite the promising performance, the tailored model still suffers from foot skating which is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we identify foot-ground contact and correct foot motions along the denoising process. By organically combining these well-designed components together, we present StableMoFusion, a robust and efficient framework for human motion generation. Extensive experimental results show that our StableMoFusion performs favorably against current state-of-the-art methods.
Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white noise and incorporate random noise at each generative step, leading to over-smoothed results. In this paper, we present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep. Additionally, we start the restoration process with an initialization combined with the measurement signal, providing supplementary information to better align the generative process. Extensive experimental results and analyses validate that our proposed method significantly enhances image restoration performance while consuming negligible additional computational resources.
Although the diffusion model has achieved remarkable performance in the field of image generation, its high inference delay hinders its wide application in edge devices with scarce computing resources. Therefore, many training-free sampling methods have been proposed to reduce the number of sampling steps required for diffusion models. However, they perform poorly under a very small number of sampling steps. Thanks to the emergence of knowledge distillation technology, the existing training scheme methods have achieved excellent results at very low step numbers. However, the current methods mainly focus on designing novel diffusion model sampling methods with knowledge distillation. How to transfer better diffusion knowledge from teacher models is a more valuable problem but rarely studied. Therefore, we propose Relational Diffusion Distillation (RDD), a novel distillation method tailored specifically for distilling diffusion models. Unlike existing methods that simply align teacher and student models at pixel level or feature distributions, our method introduces cross-sample relationship interaction during the distillation process and alleviates the memory constraints induced by multiple sample interactions. Our RDD significantly enhances the effectiveness of the progressive distillation framework within the diffusion model. Extensive experiments on several datasets (e.g., CIFAR-10 and ImageNet) demonstrate that our proposed RDD leads to 1.47 FID decrease under 1 sampling step compared to state-of-the-art diffusion distillation methods and achieving 256x speed-up compared to DDIM strategy. Code is available at https://github.com/cantbebetter2/RDD.
Equilibrated Diffusion: Frequency-aware Textual Embedding for Equilibrated Image Customization
PDF ↗Image customization involves learning the subject from provided concept images and generating it within textual contexts, typically yielding alterations of attributes such as style or background. Prevailing methods primarily rely on fine-tuning technique, wherein a unified latent embedding is employed to characterize various concept attributes. However, the attribute entanglement renders customized result challenging to mitigate the influence of subject-irrelevant attributes (e.g., style and background). To overcome these issues, we propose Equilibrated Diffusion, an innovative method that achieves equilibrated image customization by decoupling entangled concept attributes from a frequency-aware perspective, thus harmonizing textual and visual consistency. Unlike conventional approaches that employ a shared latent embedding and tuning process to learn concept, our Equilibrated Diffusion draws inspiration from the correlation between high- and low-frequency components with image style and content, decomposing concept accordingly in the frequency domain. Through independently optimizing concept embeddings in the frequency domain, the denoising model not only enriches its comprehension of style attribute irrelevant to subject identity but also inherently augments its aptitude for accommodating novel stylized descriptions. Furthermore, by combining different frequency embeddings, our model retains the spatially original customization capability. We further design a diffusion process guided by subject masks to alleviate the influence of background attribute, thereby strengthening text alignment. To ensure subject-related information consistency, Residual Reference Attention (RRA) is incorporated into the denoising model of spatial attention computation, effectively preserving structural details. Experimental results demonstrate that Equilibrated Diffusion surpasses other competitors with better subject consistency while closely adhering to text descriptions, thus validating the superiority of our approach. The code is available at https://github.com/maple-research-lab/EqDiff.
Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for modeling both graph structures and features, making them a focal point in multi-view learning research. However, these methods typically only account for static data dependencies within each view separately when constructing the topology necessary for GCNs, overlooking potential relationships across views in multi-view data. Furthermore, there is a notable absence of theoretical guidance for constructing multi-view data topologies, leading to uncertainty regarding the progression of graph embeddings toward a consistent state. To tackle these challenges, we introduce a framework named energy-constrained multi-view graph diffusion. This approach establishes a mathematical correspondence between multi-view data and GCNs via graph diffusion. It treats multi-view data as a unified entity and devises a feature propagation process with inter-view awareness by considering both inter-view and intra-view feature flow across the entire system. Additionally, an energy function is introduced to guide the inter- and intra-view diffusion, ensuring that the representations converge towards global consistency. The empirical research on several benchmark datasets substantiates the benefits of the proposed method.
Pansharpening is an important technique for remote sensing imaging systems to obtain high-resolution multispectral images. Existing deep learning-based methods mostly rely on using pseudo-groundtruth multi-spectral images for supervised learning. The whole training process only remains at the scale of reduced resolution, which means that the impact of the degradation process is ignored and high-quality images cannot be guaranteed at full resolution. To address the challenge, we propose a new unsupervised framework that does not rely on pseudo-groundtruth but uses the invariance of the degradation process to build a consistent loss function on the original scale for network training. Specifically, we first introduce the operator learning method to build an exact mapping function from multi-spectral to panchromatic images and decouple both spectral and texture features. Then, through joint training, operators and convolutional networks can learn the spatial degradation process and spectral degradation process at full resolution, respectively. By introducing them to build consistency constraints, we can train the pansharpening network at the original full resolution. Our approach can be applied to existing pansharpening methods, improving their usability on original data, which matches practical application requirements. The experimental results on different kinds of satellite datasets demonstrate that the proposed network outperforms state-of-the-art methods both visually and quantitatively. Our code is available at https://github.com/quycruin/Qvac.
Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD problem by considering the characteristic (i.e., boundary) of shadow. Motivated by this, we propose a Timeline and Boundary Guided Diffusion (TBGDiff) network for VSD where we take account of the past-future temporal guidance and boundary information jointly. In detail, we design a Dual Scale Aggregation (DSA) module for better temporal understanding by rethinking the affinity of the long-term and short-term frames for the clipped video. Next, we introduce Shadow Boundary Aware Attention (SBAA) to utilize the edge contexts for capturing the characteristics of shadows. Moreover, we are the first to introduce the Diffusion model for VSD in which we explore a Space-Time Encoded Embedding (STEE) to inject the temporal guidance for Diffusion to conduct shadow detection. Benefiting from these designs, our model can not only capture the temporal information but also the shadow property. Extensive experiments show that the performance of our approach overtakes the state-of-the-art methods, verifying the effectiveness of our components. We release the codes at https://github.com/haipengzhou856/TBGDiff.
Icons are ubiquitous visual elements in graphic design, yet their creation is often complex and time-consuming. To resolve this problem, we draw inspiration from the booming text-to-image field and propose Text-Guided Icon Set Expansion, a novel task that helps users design high-quality icons using textual descriptions. Besides, users can control the style consistency of the created icons by inputting a few hand-crafted icons as style reference. Despite its practicality, the task poses two unique challenges. (i) Abstract Concept Visualization. Abstract concepts like technology and health are frequently encountered in icon creation, but their visualization is not straightforward and requires a grounding process that translates them into physical, easy-to-depict objects. (ii) Fine-grained Style Transfer. Unlike ordinary images, icons exhibit richer fine-grained stylistic elements, including tones, line widths, shapes, shadow effects, etc., which puts higher demands on capturing and preserving detailed styles during icon generation. To address the challenges, we propose IconDM, a method based on pre-trained text-to-image (T2I) diffusion models. Our approach incorporates a one-time domain adaptation process and an online style transfer process. In domain adaptation, we enhance the existing T2I model's capability to understand abstract concepts by fine-tuning it on high-quality icon-text pairs. To achieve this, we construct a large-scale dataset IconBank containing 2.3 million icon samples, and leverage a state-of-the-art vision-language model to generate textual descriptions for each icon. In style transfer, we introduce a Style Enhancement Module into the T2I model. It explicitly extracts the fine-grained style features from the given reference icons and is jointly optimized with the T2I model during DreamBooth tuning. To assess IconDM, we present IconBench, a structured evaluation suite with 30 icon sets and 100 concepts (including 50 abstract concepts). Quantitative results, qualitative analysis, and extensive ablation studies demonstrate the effectiveness of IconDM.
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. Our code, model and datasets ara available at https://onechartt.github.io.
Traditional dense video captioning predominantly focuses on edited exocentric footage. These videos are filmed from an external perspective and generally feature distinct transitions between different events, as exemplified in edited instructional videos. However, such videos do not genuinely reflect the way we perceive our real lives. Instead, we observe the world from an egocentric viewpoint and witness only continuous unedited footage. To facilitate further research, we introduce a new topic: Egocentric Vehicle Dense Video Captioning, in classic vehicle driving scenarios. This is a multi-modal, multi-task subject endeavor for a comprehensive understanding of untrimmed, egocentric driving videos. It consists of three sub-tasks that concentrate on event localization, captioning, and vehicle state estimation separately. To accomplish these tasks, it is necessary to deal with at least three challenges: extracting ego-motion relevant information, describing driving behavior and analyzing the underlying rationale, as well as resolving the boundary ambiguity problem. In response, we devise corresponding solutions, including a vehicle ego-motion learning strategy and a novel adjacent contrastive learning strategy, which effectively address the aforementioned issues. We validate our method by conducting extensive experiments on the BDD-X dataset, all of which show promising results and achieve new state-of-the-art performance on most metrics, which proves the effect of our approach.