Image deblurring aims to restore a high-quality image from its corresponding blurred. The emergence of CNNs and Transformers has enabled significant progress. However, these methods often face the dilemma between eliminating long-range degradation perturbations and maintaining computational efficiency. While the selective state space model (SSM) shows promise in modeling long-range dependencies with linear complexity, it also encounters challenges such as local pixel forgetting and channel redundancy. To address this issue, we propose an efficient image deblurring network that leverages selective state spaces model to aggregate enriched and accurate features. Specifically, we introduce an aggregate local and global information block (ALGBlock) designed to effectively capture and integrate both local invariant properties and non-local information. The ALGBlock comprises two primary modules: a module for capturing local and global features (CLGF), and a feature aggregation module (FA). The CLGF module is composed of two branches: the global branch captures long-range dependency features via a selective state spaces model, while the local branch employs simplified channel attention to model local connectivity, thereby reducing local pixel forgetting and channel redundancy. In addition, we design a FA module to accentuate the local part by recalibrating the weight during the aggregation of the two branches for restoration. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on widely used benchmarks.
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Supervised cross-modal retrieval (CMR) achieves excellent performance thanks to the semantic information provided by its labels, which helps to establish semantic correlations between samples from different modalities. However, in real-world scenarios, there often exists a large amount of unlabeled and unpaired multimodal training data, rendering existing methods unfeasible. To address this issue, we propose a novel partially aligned cross-modal retrieval method called Optimal Transport-based Prototype Alignment Learning (OTPAL). Due to the high computational complexity involved in directly establishing matching correlations between unannotated unaligned cross-modal samples, instead, we establish matching correlations between shared prototypes and samples. To be specific, we employ the optimal transport algorithm to establish cross-modal alignment information between samples and prototypes, and then minimize the distance between samples and their corresponding prototypes through a specially designed prototype alignment loss. As an extension of this paper, we also extensively investigate the influence of incomplete multimodal data on cross-modal retrieval performance under the partially aligned setting proposed above. To further address the above more challenging scenario, we raise a scalable prototype-based neighbor feature completion method, which better captures the correlations between incomplete samples and neighbor samples through a cross-modal self-attention mechanism. Experimental results on four benchmark datasets show that our method can obtain satisfactory accuracy and scalability in various real-world scenarios.
Panoramic Activity Recognition (PAR) aims to identify multi-granul-arity behaviors performed by multiple persons in panoramic scenes, including individual activities, group activities, and global activities. Previous methods 1) heavily rely on manually annotated detection boxes in training and inference, hindering further practical deployment; or 2) directly employ normal detectors to detect multiple persons with varying size and spatial occlusion in panoramic scenes, blocking the performance gain of PAR. To this end, we consider learning a detector adapting varying-size occluded persons, which is optimized along with the recognition module in the all-in-one framework. Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way. Specifically, to accommodate the varying sizes and spatial occlusion of multiple persons in crowed panoramic scenes, we introduce a panoramic adapt-focuser, achieving the size-adapting detection of individuals by comprehensively selecting and performing fine-grained detections on object-dense sub-regions identified through original detections. In addition, to mitigate information loss due to inaccurate individual localizations, we introduce a bi-propagation prototyper that promotes closed-loop interaction and informative consistency across different granularities by facilitating bidirectional information propagation among the individual, group, and global levels. Extensive experiments demonstrate the significant performance of AdaFPP and emphasize its powerful applicability for PAR.
Recent research on Diffusion Models and Transformers has brought significant advancements to 3D Human Pose Estimation (HPE). Nonetheless, existing methods often fail to concurrently address the issues of accuracy and generalization. In this paper, we propose a Geometry-guided Dif fusion Model with Masked Transformer (Masked Gifformer) for robust multi-view 3D HPE. Within the framework of the diffusion model, a hierarchical multi-view trans-former-based denoiser is exploited to fit the 3D pose distribution by systematically integrating joint and view information. To address the long-standing problem of poor generalization, we introduce a fully random mask mechanism without any additional learnable modules or parameters. Furthermore, we incorporate geometric guidance into the diffusion model to enhance the accuracy of the model. This is achieved by optimizing the sampling process to minimize reprojection errors through modeling a conditional guidance distribution. Extensive experiments on two benchmarks demonstrate that Masked Gifformer effectively achieves a trade-off between accuracy and generalization. Specifically, our method outperforms other probabilistic methods by > 40% and achieves comparable results with state-of-the-art deterministic methods. In addition, our method exhibits robustness to varying camera numbers, spatial arrangements, and datasets.
In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements.
Neuromorphic event sensors are novel visual cameras that feature high-speed illumination-variation sensing and have found widespread application in guiding frame-based imaging enhancement. This paper focuses on color restoration in the event-guided image deblurring task, we fuse blurry images with mosaic color events instead of mono events to avoid artifacts such as color bleeding. The challenges associated with this approach include demosaicing color events for reconstructing full-resolution sampled signals and fusing bimodal signals to achieve image deblurring. To meet these challenges, we propose a novel network called Color4E to enhance the color restoration quality for the image deblurring task. Color4E leverages an event demosaicing module to upsample the spatial resolution of mosaic color events and a cross-encoding image deblurring module for fusing bimodal signals, a refinement module is designed to fuse full-color events and refine initial deblurred images. Furthermore, to avoid the real-simulated gap of events, we implement a display-filter-camera system that enables mosaic and full-color event data captured synchronously, to collect a real-captured dataset used for network training and validation. The results on the public dataset and our collected dataset show that Color4E enables high-quality event-based image deblurring compared to state-of-the-art methods.
Domain generalization 3D segmentation aims to learn the point clouds with unknown distributions. Feature augmentation has been proven to be effective for domain generalization. However, each point of the 3D segmentation scene contains uncertainty in the target domain, which affects model generalization. This paper proposes the Domain Generalization-Aware Uncertainty Introspective Learning (DGUIL) method, including Potential Uncertainty Modeling (PUM) and Momentum Introspective Learning (MIL), to deal with the point uncertainty in domain shift. Specifically, PUM explores the underlying uncertain point cloud features and generates the different distributions for each point. The PUM enhances the point features over an adaptive range, which provides various information for simulating the distribution of the target domain. Then, MIL is designed to learn generalized feature representation in uncertain distributions. The MIL utilizes uncertainty correlation representation to measure the predicted divergence of knowledge accumulation, which learns to carefully judge and understand divergence through uncertainty introspection loss. Finally, extensive experiments verify the advantages of the proposed method over current state-of-the-art methods. The code will be available at https://github.com/ChicalH/DGUIL.
With the popularity and advancement of the Internet and video-sharing platforms, video affective content analysis has greatly developed. Temporal information is crucial for this task. Nevertheless, existing methods often overlook the fact that there is substantial irrelevant information in videos and that the importance of modalities is uneven for emotional tasks. This could result in noise from both temporal fragments and modalities, reducing the model's ability to identify crucial temporal fragments and recognize emotions. To tackle the above issues, we propose a Temporal Enhancement (TE) method in this paper. Specifically, we utilize three encoders for extracting features at various levels and employ temporal sampling to enhance the temporal data, thereby enriching video representation and improving the model's robustness to noise. Subsequently, we design a cross-modal temporal enhancement module to enhance temporal information for every modal feature. This module interacts with multiple modalities simultaneously to emphasize critical temporal fragments while suppressing irrelevant ones. The experimental results on four benchmark datasets show that the proposed temporal enhancement method achieves state-of-the-art video affective content analysis performance. Moreover, the effectiveness of each module is confirmed through ablation experiments.
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by the process through which the human brain handles emotions and the theory of cross-modal plasticity, we propose UMBEnet, a brain-like unified modal affective processing network. The primary design of UMBEnet includes a Dual-Stream (DS) structure that fuses inherent prompts with a Prompt Pool and a Sparse Feature Fusion (SFF) module. The design of the Prompt Pool is aimed at integrating information from different modalities, while inherent prompts are intended to enhance the system's predictive guidance capabilities and effectively manage knowledge related to emotion classification. Moreover, considering the sparsity of effective information across different modalities, the SSF module aims to make full use of all available sensory data through the sparse integration of modality fusion prompts and inherent prompts, maintaining high adaptability and sensitivity to complex emotional states. Extensive experiments on the largest benchmark datasets in the Dynamic Facial Expression Recognition (DFER) field, including DFEW, FERV39k, and MAFW, have proven that UMBEnet consistently outperforms the current state-of-the-art methods. Notably, in scenarios of Modality Missingness and multimodal contexts, UMBEnet significantly surpasses the leading current methods, demonstrating outstanding performance and adaptability in tasks that involve complex emotional understanding with rich multimodal information. Code can be obtained at https://github.com/Xinji-Mai/UMBEnet.
Multimodal Multi-Label Emotion Recognition (MMER) aims to identify one or more emotion categories expressed by an utterance of a speaker. Despite obtaining promising results, previous studies on MMER represent each emotion category using a one-hot vector and ignore the intrinsic relations between emotions. Moreover, existing works mainly learn the unimodal representation based on the multimodal supervision signal of a single sample, failing to explicitly capture the unique emotional state of each modality as well as its emotional correlation between samples. To overcome these issues, we propose a Unimodal Valence-Arousal driven contrastive learning framework (UniVA) for the MMER task. Specifically, we adopt the valence-arousal (VA) space to represent each emotion category and regard the emotion correlation in the VA space as priors to learn the emotion category representation. Moreover, we employ pre-trained unimodal VA models to obtain the VA scores for each modality of the training samples, and then leverage the VA scores to construct positive and negative samples, followed by applying supervised contrastive learning to learn the VA-aware unimodal representations for multi-label emotion prediction. Experimental results on two benchmark datasets MOSEI and M3ED show that the proposed UniVA framework consistently outperforms a number of existing methods for the MMER task. The source code is publicly released at https://github.com/NUSTM/UniVA.
In the literature, existing studies on text-to-motion generation (TMG) routinely focus on exploring the objective alignment of text and motion, which largely ignore the subjective emotion information, especially the limb-level emotion information. With this in mind, this paper proposes a new Emotion-enriched Text-to-Motion Generation (ETMG) task, aiming to generate motions with the subjective emotion information. Further, this paper believes that injecting emotions into limbs (named intra-limb emotion injection) and ensuring the coordination and coherence of emotional motions after injecting emotion information (named inter-limb emotion disturbance) is rather important and challenging in this ETMG task. To this end, this paper proposes an LL M-guided Limb-level Emotion Manipulating ( L3 EM) approach to ETMG. Specifically, this approach designs an LLM-guided intra-limb emotion modeling block to inject emotion into limbs, followed by a graph-structured inter-limb relation modeling block to ensure the coordination and coherence of emotional motions. Particularly, this paper constructs a coarse-grained Emotional Text-to-Motion (EmotionalT2M) dataset and a fine-grained Limb -level Emotional Text-to-Motion (Limb-ET2M) dataset to justify the effectiveness of the proposed L3EM approach. Detailed evaluation demonstrates the significant advantage of our L3EM approach to ETMG over the state-of-the-art baselines. This justifies the importance of the limb-level emotion information for ETMG and the effectiveness of our L3EM approach in coherently manipulating such information.
Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved notable improvements by leveraging the knowledge embedded within pre-trained visual models, the lack of direct association between factual-level features and emotional categories, called the ''affective gap'', limits the applicability of pre-training knowledge for VER tasks. On the contrary, the explicit emotional expression and high information density in textual modality eliminate the ''affective gap''. Therefore, we propose borrowing the knowledge from the pre-trained textual model to enhance the emotional perception of pre-trained visual models. We focus on the factual and emotional connections between images and texts in noisy social media data, and propose Partitioned Adaptive Contrastive Learning (PACL) to leverage these connections. Specifically, we manage to separate different types of samples and devise distinct contrastive learning strategies for each type. By dynamically constructing negative and positive pairs, we fully exploit the potential of noisy samples. Through comprehensive experiments, we demonstrate that bridging the "affective gap'' significantly improves the performance of various pre-trained visual models in downstream emotion-related tasks. Our code is released on https://github.com/wdqqdw/PACL.
In real-world recon-videos such as surveillance and drone reconnaissance videos, commonly used explicit language, acoustic and facial expressions information is often missing. However, these videos are always rich in anomalous sentiments (e.g., criminal tendencies), which urgently requires the implicit scene information (e.g., actions and object relations) to fast and precisely identify these anomalous sentiments. Motivated by this, this paper proposes a new chat-paradigm Implicit anomalous sentiment Discovering and grounding (IasDig) task, aiming to interactively, fast discovering and grounding anomalous sentiments in recon-videos via leveraging the implicit scene information (i.e., actions and object relations). Furthermore, this paper believes that this IasDig task faces two key challenges, i.e., scene modeling and scene balancing. To this end, this paper proposes a new Scene-enhanced Video Large Language Model named Hawkeye, i.e., acting like a raptor (e.g., a Hawk) to discover and locate prey, for the IasDig task. Specifically, this approach designs a graph-structured scene modeling module and a balanced heterogeneous MoE module to address the above two challenges, respectively. Extensive experimental results on our constructed scene-sparsity and scene-density IasDig datasets demonstrate the great advantage of Hawkeye to IasDig over the advanced Video-LLM baselines, especially on the metric of false negative rates. This justifies the importance of the scene information for identifying implicit anomalous sentiments and the impressive practicality of Hawkeye for real-world applications.
Multimodal audio-image music transcription has been recently posed as a means of retrieving a digital score representation by leveraging the individual estimations from Automatic Music Transcription (AMT)---acoustic recordings---and Optical Music Recognition (OMR)---image scores---systems. Nevertheless, while proven to outperform single-modality recognition rates, this approach has been exclusively validated under controlled scenarios---monotimbral and monophonic synthetic data---mainly due to a lack of collections with symbolic score-level annotations for both recordings and graphical sheets. To promote research on this topic, this work presents the Multimodal mUSic Collection for Automatic Transcription (MUSCAT) assortment of acoustic recordings, image sheets, and their score-level annotations in several notation formats. This dataset comprises almost 80 hours of real recordings with varied instrumentation and polyphony degrees---ranging from piano to orchestral music---, 1251 scanned sheets, and 880 symbolic scores from 37 composers, which may also be used in other tasks involving metadata such as instrument identification or composer recognition. A fragmented subset of this collection solely focused on acoustic data for score-level AMT---the MUSic Collection for aUtomatic Transcription - fragmented Subset (MUSCUTS) assortment---is also presented together with a baseline experimentation, concluding the need to foster research on this field with real recordings. Finally, a web-based service is also provided to increase the size of the collections collaboratively.
Video-to-audio generation is crucial for autonomous video editing and post-processing, which aims to generate high-quality audio for silent videos with semantic similarity and temporal synchronization. However, most existing methods mainly focus on matching the semantics of the visual and acoustic modalities while merely considering their temporal alignment in a coarse granularity, thus failing to achieve precise synchronization. In this study, we propose a novel time-aligned video-to-audio framework, called TiVA, to achieve semantic matching and temporal synchronization jointly when generating audio. Given a silent video, our method encodes its visual semantics and predicts an audio layout separately. Then, leveraging the semantic latent embeddings and the predicted audio layout as condition, it learns a latent diffusion-based audio generator. Comprehensive objective and subjective experiments demonstrate that our method consistently outperforms state-of-the-art methods on semantic matching and temporal synchronization.
Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization
PDF ↗Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of audio from text prompts is an important aspect of such processes in the music and film industry. Many of the recent diffusion-based text-to-audio models focus on training increasingly sophisticated diffusion models on a large set of datasets of prompt-audio pairs. These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt. Our hypothesis is focusing on how these aspects of audio generation could improve audio generation performance in the presence of limited data. As such, in this work, using an existing text-to-audio model Tango, we synthetically create a preference dataset where each prompt has a winner audio output and some loser audio outputs for the diffusion model to learn from. The loser outputs, in theory, have some concepts from the prompt missing or in an incorrect order. We fine-tune the publicly available Tango text-to-audio model using diffusion-DPO (direct preference optimization) loss on our preference dataset and show that it leads to improved audio output over Tango and AudioLDM2, in terms of both automatic- and manual-evaluation metrics.
Recent AIGC systems possess the capability to generate digital multimedia content based on human language instructions, such as text, image and video. However, when it comes to speech, existing methods related to human instruction-to-speech generation exhibit two limitations. Firstly, they require the division of inputs into content prompt (transcript) and description prompt (style and speaker), instead of directly supporting human instruction. This division is less natural in form and does not align with other AIGC models. Secondly, the practice of utilizing an independent description prompt to model speech style, without considering the transcript content, restricts the ability to control speech at a fine-grained level. To address these limitations, we propose VoxInstruct, a novel unified multilingual codec language modeling framework that extends traditional text-to-speech tasks into a general human instruction-to-speech task. Our approach enhances the expressiveness of human instruction-guided speech generation and aligns the speech generation paradigm with other modalities. To enable the model to automatically extract the content of synthesized speech from raw text instructions, we introduce speech semantic tokens as an intermediate representation for instruction-to-content guidance. We also incorporate multiple Classifier-Free Guidance (CFG) strategies into our codec language model, which strengthens the generated speech following human instructions. Furthermore, our model architecture and training strategies allow for the simultaneous support of combining speech prompt and descriptive human instruction for expressive speech synthesis, which is a first-of-its-kind attempt. Codes, models and demos are at: https://github.com/thuhcsi/VoxInstruct.
Singing melody extraction is a key task in the field of music information retrieval (MIR). However, decades of research works have uncovered two difficult issues. First, binary classification on frequency-domain audio features (e.g., spectrogram) is regarded as the primary method, which ignores the potential associations of musical information at different frequency bins, as well as their varying significance for output decisions. Second, the existing semi-supervised singing melody extraction models ignore the accuracy of the generated pseudo labels by semi-supervised models, which largely limits the further improvements of the model. To solve the two issues, in this paper, we propose a heterogeneous knowledge distillation framework for semi-supervised singing melody extraction using harmonic supervision, termed as HKDSME. We begin by proposing a four-class classification paradigm for determining the results of singing melody extraction using harmonic supervision. This enables the model to capture more information regarding melodic relations in spectrograms. To improve the accuracy issue of pseudo labels, we then build a semi-supervised method by leveraging the extracted harmonics as a consistent regularization. Different from previous methods, it judges the availability of unlabeled data in terms of the inner positional relations of extracted harmonics. To further build a light-weight semi-supervised model, we propose a heterogeneous knowledge distillation (HKD) module, which enables the prior knowledge to transfer between heterogeneous models. We also propose a novel confidence guided loss, which incorporates with the proposed HKD module to reduce the wrong pseudo labels. We evaluate our proposed method using several well-known public available datasets, and the findings demonstrate the efficacy of our proposed method.
We devise an articulatory representation-based text-to-speech (TTS) model, ArtSpeech, an explainable and effective network for human-like speech synthesis, by revisiting the sound production system. Current deep TTS models learn acoustic-text mapping in a fully parametric manner, ignoring the explicit physical significance of articulation movement. ArtSpeech, on the contrary, leverages articulatory representations to perform adaptive TTS, clearly describing the voice tone and speaking prosody of different speakers. Specifically, energy, F0, and vocal tract variables are utilized to represent airflow forced by articulatory organs, the degree of tension in the vocal folds of the larynx, and the coordinated movements between different organs, respectively. We also design a multi-dimensional style mapping network to extract speaking styles from the articulatory representations, guided by which variation predictors could predict the final mel spectrogram output. To validate the effectiveness of our approach, we conducted comprehensive experiments and analyses using the widely recognized speech corpus, such as LJSpeech and LibriTTS datasets, yielding promising similarity enhancement between the generated results and the target speaker's voice and prosody.
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
PDF ↗Large Vision-Language Models (LVLMs) exhibit remarkable capabilities but struggle with ''hallucinations''-inconsistencies between images and their descriptions. Previous hallucination evaluation studies on LVLMs have identified hallucinations in terms of objects, attributes, and relations but overlooked complex hallucinations that create an entire narrative around a fictional entity. In this paper, we introduce a refined taxonomy of hallucinations, featuring a new category: Event Hallucination. We then utilize advanced LLMs to generate and filter fine-grained hallucinatory data consisting of various types of hallucinations, with a particular focus on event hallucinations, laying the groundwork for integrating discriminative and generative evaluation methods within our universal evaluation framework. The proposed benchmark distinctively assesses LVLMs' ability to tackle a broad spectrum of hallucinations, making it a reliable and comprehensive tool for gauging LVLMs' efficacy in handling hallucinations. We will release our code and data.