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Wenbo Huang 0001, Jinghui Zhang 0001, Xuwei Qian, Zhen Wu 0001, Meng Wang 0009, Lei Zhang 0130

High frame-rate~(HFR) videos of action recognition improve fine-grained expression while reducing the spatio-temporal relation and motion information density. Thus, large amounts of video samples are continuously required for traditional data-driven training. However, samples are not always sufficient in real-world scenarios, promoting few-shot action recognition~(FSAR) research. We observe that most recent FSAR works build spatio-temporal relation of video samples via temporal alignment after spatial feature extraction, cutting apart spatial and temporal features within samples. They also capture motion information via narrow perspectives between adjacent frames without considering density, leading to insufficient motion information capturing. Therefore, we propose a novel plug-and-play architecture for FSAR called Spatio-tempOral frAme tuPle enhancer (SOAP) in this paper. The model we designed with such architecture refers to SOAP-Net. Temporal connections between different feature channels and spatio-temporal relation of features are considered instead of simple feature extraction. Comprehensive motion information is also captured, using frame tuples with multiple frames containing more motion information than adjacent frames. Combining frame tuples of diverse frame counts further provides a broader perspective. SOAP-Net achieves new state-of-the-art performance across well-known benchmarks such as SthSthV2, Kinetics, UCF101, and HMDB51. Extensive empirical evaluations underscore the competitiveness, pluggability, generalization, and robustness of SOAP. The code is released at https://github.com/wenbohuang1002/SOAP.

Zhidong Yu, Zhenbo Shi, Xiaoman Liu, Wei Yang 0011

Recent research has confirmed the possibility of adversarial attacks on deep models. However, these methods typically assume that the surrogate model has access to the target domain, which is difficult to achieve in practical scenarios. To address this limitation, this paper introduces a novel cross-domain attack method tailored for semantic segmentation, named Prototype-based Feature and Frequency Alteration Attack (PFFAA). This approach empowers a surrogate model to efficiently deceive the black-box victim model without requiring access to the target data. Specifically, through limited queries on the victim model, bidirectional relationships are established between the target classes of the victim model and the source classes of the surrogate model, enabling the extraction of prototypes for these classes. During the attack process, the features of each source class are perturbed to move these features away from their respective prototypes. Moreover, we propose substituting frequency information from images used to train the surrogate model into the frequency domain of the test images to modify texture and structure, thus further enhancing the attack efficacy. Experimental results across multiple datasets and victim models validate that PFFAA achieves state-of-the-art performances.

Yanshan Zhou, Pingrui Lai, Jiaqi Yu, Yingjie Xiong, Hua Yang 0001

With global occurrences of crowd crushes and stampedes, dense crowd simulation has been drawing great attention. In this research, our goal is to simulate dense crowd motions under six classic motion patterns, more specifically, to generate subsequent motions of dense crowds from the given initial states. Since dense crowds share similarities with fluids, such as continuity and fluidity, one common approach for dense crowd simulation is to construct hydrodynamics-based models, which consider dense crowds as fluids, guide crowd motions with Navier-Stokes equations, and conduct dense crowd simulation by solving governing equations. Despite the proposal of these models, dense crowd simulation faces multiple challenges, including the difficulty of directly solving Navier-Stokes equations due to their nonlinear nature, the ignorance of distinctive crowd characteristics which fluids lack, and the gaps in the evaluation and validation of crowd simulation models. To address the above challenges, we build a hydrodynamic model, which captures the crowd physical properties (continuity, fluidity, etc.) with Navier-Stokes equations and reflects the crowd social properties (sociality, personality, etc.) with operators that describe crowd interactions and crowd-environment interactions. To tackle the computational problem, we propose to solve the governing equation based on Navier-Stokes equations using neural networks, and introduce the Hydrodynamics-Informed Neural Network (HINN) which preserves the structure of the governing equation in its network architecture. To facilitate the evaluation, we construct a new dense crowd motion video dataset called Dense Crowd Flow Dataset (DCFD), containing six classic motion patterns (line, curve, circle, cross, cluster and scatter) and 457 video clips, which can serve as the groundtruths for various objective metrics. Numerous experiments are conducted using HINN to simulate dense crowd motions under six motion patterns with video clips from DCFD. Objective evaluation metrics that concerns authenticity, fidelity and diversity demonstrate the superior performance of our model in dense crowd simulation compared to other simulation models. Our code and dataset are available at https://github.com/shanshan-zys/HINN.

Chaomin Shen 0001, Yaomin Huang, Haokun Zhu, Jinsong Fan, Guixu Zhang

Knowledge distillation has become widely recognized for its ability to transfer knowledge from a large teacher network to a compact and more streamlined student network. Traditional knowledge distillation methods primarily follow a teacher-oriented paradigm that imposes the task of learning the teacher's complex knowledge onto the student network. However, significant disparities in model capacity and architectural design hinder the student's comprehension of the complex knowledge imparted by the teacher, resulting in sub-optimal performance. This paper introduces a novel perspective emphasizing student-oriented and refining the teacher's knowledge to better align with the student's needs, thereby improving knowledge transfer effectiveness. Specifically, we present the Student-Oriented Knowledge Distillation (SoKD), which incorporates a learnable feature augmentation strategy during training to refine the teacher's knowledge of the student dynamically. Furthermore, we deploy the Distinctive Area Detection Module (DAM) to identify areas of mutual interest between the teacher and student, concentrating knowledge transfer within these critical areas to avoid transferring irrelevant information. This customized module ensures a more focused and effective knowledge distillation process. Our approach, functioning as a plug-in, could be integrated with various knowledge distillation methods. Extensive experimental results demonstrate the efficacy and generalizability of our method.

Jiacheng Zhang, Jie Wu 0030, Huafeng Kuang, Haiming Zhang 0001, Yuxi Ren, Weifeng Chen, Manlin Zhang, Xuefeng Xiao 0001, Guanbin Li

Recently, there has been significant progress in leveraging human feedback to enhance diffusion-based image generation, garnering considerable interest and attention. However, existing methods fail to achieve a fine-grained performance boost for the following challenges: i) insufficient amount of fine-grained feedback data; ii) lack of effective fine-grained feedback learning framework; To tackle these challenges, we present TreeReward to facilitate the fine-grained feedback optimization for diffusion models. Specifically, to address the limitation of the fine-grained feedback data, we first design a novel "AI + Expert" feedback data construction pipeline, yielding about 2.2M high-quality feedback dataset encompassing six fine-grained dimensions at a relatively low cost. Built upon this dataset, we introduce a tree-structure reward model to exploit the fine-grained feedback data efficiently and provide tailored optimization during feedback learning. We validate the feedback learning performance of our method across different fine-grained dimensions and various downstream tasks. Extensive experiments on both Stable Diffusion v1.5 (SD1.5) and Stable Diffusion XL (SDXL) demonstrate the effectiveness of our method in enhancing the general and fine-grained generation and downstream tasks generalization.

Ruoxi Deng, Bin Yu, Jinxuan Lu, Caixia Zhou, Zhao-Min Chen, Jie Hu 0041

Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a novel solution to bolster SED through the encoding of both language and image data. Distinct from antecedent language-driven techniques, which predominantly utilize static elements such as dataset labels, our method taps into the dynamic language content that details the objects in each image and their interrelations. By encoding this varied input, we generate integrated features that utilize semantic insights to refine the high-level image features and the ultimate mask representations. This advancement improves the quality of these features and elevates SED performance. Experimental evaluation on benchmark datasets, including SBD and Cityscape, showcases the efficacy of our method, achieving leading ODS F-scores of 79.0 and 76.0, respectively. Our approach signifies a notable advancement in SED technology by seamlessly integrating multimodal textual information, embracing both static and dynamic aspects.

Wenjie Li, Heng Guo 0003, Xuannan Liu, Kongming Liang, Jiani Hu, Zhanyu Ma, Jun Guo 0002

Face super-resolution aims to reconstruct a high-resolution face image from a low-resolution face image. Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably introduces distortions, especially to high-frequency features such as edges. To address this issue, we propose a wavelet-based feature enhancement network, which mitigates feature distortion by losslessly decomposing the input feature into high and low-frequency components using the wavelet transform and processing them separately. To improve the efficiency of facial feature extraction, a full domain Transformer is further proposed to enhance local, regional, and global facial features. Such designs allow our method to perform better without stacking many modules as previous methods did. Experiments show that our method effectively balances performance, model size, and speed. Code link: https://github.com/PRIS-CV/WFEN.

Rui Xu, Gaolei Li, Changze Li, Zhaohui Yang 0001, Yuchen Liu 0001, Mingzhe Chen

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, existing methods primarily focus on global scene reconstruction using large datasets, which necessitate substantial computational resources and impose high-quality requirements on input images. Nevertheless, in practical applications, users prioritize the 3D reconstruction results of on-demand specific object (OSO) based on their individual demands . Furthermore, the collected images transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of NeRF reconstruction, thereby limiting its scalability. In this paper, we propose a novel on-demand Semantic Neural Radiance Fields (OSNeRF) scheme, which offers fast and robust 3D object reconstruction for diverse tasks. Within OSNeRF, semantic encoder is employed to extract core semantic features of OSOs from the collected scene images, semantic decoder is utilized to facilitate robust image recovery under HIWE conditions, lightweight renderer is employed for fast and efficient object reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed OSNeRF enables fast and robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality.

Kunyu Peng, David Schneider 0006, Alina Roitberg, Kailun Yang 0001, Jiaming Zhang 0001, Chen Deng, Kaiyu Zhang, M. Saquib Sarfraz, Rainer Stiefelhagen

In this paper, we tackle the new task of video-based Activated Muscle Group Estimation (AMGE) aiming at identifying active muscle regions during physical activity in the wild. To this intent, we provide the MuscleMap dataset featuring >15K video clips with 135 different activities and 20 labeled muscle groups. This dataset opens the vistas to multiple video-based applications in sports and rehabilitation medicine under flexible environment constraints. The proposed MuscleMap dataset is constructed with YouTube videos, specifically targeting High-Intensity Interval Training (HIIT) physical exercise in the wild. To make the AMGE model applicable in real-life situations, it is crucial to ensure that the model can generalize well to numerous types of physical activities not present during training and involving new combinations of activated muscles. To achieve this, our benchmark also covers an evaluation setting where the model is exposed to activity types excluded from the training set. Our experiments reveal that the generalizability of existing architectures adapted for the AMGE task remains a challenge. Therefore, we also propose a new approach, TransM3E, which employs a multi-modality feature fusion mechanism between both the video transformer model and the skeleton-based graph convolution model with novel cross-modal knowledge distillation executed on multi-classification tokens. The proposed method surpasses all popular video classification models when dealing with both, previously seen and new types of physical activities. The database and code can be found at https://github.com/KPeng9510/MuscleMap.

Ziyi Gao 0002, Kai Chen 0027, Zhipeng Wei 0001, Tingshu Mou, Jingjing Chen 0001, Zhiyu Tan, Hao Li, Yu-Gang Jiang 0001

Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks. However, existing works on diffusion-based unrestricted attacks are mostly focused on images yet are seldom explored in videos. In this paper, we propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack (ReToMe-VA), which is the first framework to generate imperceptible adversarial video clips with higher transferability. Specifically, to achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adversarial Latent Optimization (TALO) strategy that optimizes perturbations in diffusion models' latent space at each denoising step. TALO offers iterative and accurate updates to generate more powerful adversarial frames. TALO can further reduce memory consumption in gradient computation. Moreover, to achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames in the self-attention module, resulting in temporally consistent adversarial videos. ReToMe concurrently facilitates inter-frame interactions into the attack process, inducing more diverse and robust gradients, thus leading to better adversarial transferability. Extensive experiments demonstrate the efficacy of ReToMe-VA, particularly in surpassing state-of-the-art attacks in adversarial transferability by more than 14.16% on average.

Siyuan Xu, Guannan Li, Haofei Song, Jiansheng Wang, Yan Wang 0033, Qingli Li

Immunohistochemistry (IHC) plays a crucial role in understanding disease mechanisms, diagnosing pathology and guiding treatment decisions. The precise analysis heavily depends on accurate nucleus segmentation. However, segmentation is challenging due to significant inter- and intra-nucleus variability in morphology and distribution, stemming from inherent characteristics, imaging techniques, tissue differences and other factors. While current deep learning-based methods have shown promising results, their generalization performance is limited, inevitably requiring specific training data. To address the problem, we propose a novel Ge neral framework for Nucleus Seg mentation in IHC images (GeNSeg-Net). GeNSeg-Net effectively segments nuclei across diverse tissue types and imaging techniques with high variability using a small subset for training. It comprises an enhancement model and a segmentation model. Initially, all nuclei are enhanced to a uniform morphology with distinct features by the enhancement model through generation. The subsequent segmentation task is thereby simplified, leading to higher accuracy. We design a lightweight generator and discriminator to improve both enhancement quality and computational efficiency. Extensive experiments demonstrate the effectiveness of each component within GeNSeg-Net. Compared to existing methods, GeNSeg-Net achieves state-of-the-art (SOTA) segmentation accuracy and generalization performance on both private and public datasets, while maintaining highly competitive processing speed. Code is available at https://github.com/SikangSHU/GeNSeg-Net.

Junbo Hu, Zhixin Li 0001

Transformer-based encoders that encode both region and grid features are the preferred choice for the image captioning task due to their multi-head self-attention mechanism. This mechanism ensures superior capture of relationships and contextual information between various regions in an image. However, because of the Transformer block stacking, self-attention computes the visual features several times, increasing computing costs and producing a great deal of redundant feature calculation. In this paper, we propose a novel Distilled Cross-Combination Transformer (DCCT) network. Specifically, we first design a distillation cascade fusion encoder(DCFE) to filter out redundant features in visual features that affect attentional focus, obtaining refined features. Additionally, we introduce a parallel cross-fusion attention module (PCFA) that fully utilizes the complementarity and correlation between grid and region features to better fuse the encoded dual visual features. Extensive experiments on the MSCOCO dataset demonstrate that the proposed DCCT strategy outperforms many state-of-the-art techniques and attains exceptional performance.

Yihan Zhao, Wei Xi 0003, Yuhang Cui, Gairui Bai, Xinhui Liu, Jizhong Zhao

Parameter-Efficient Fine Tuning (PEFT) has been demonstrated to be effective and efficient for transferring foundation models to downstream tasks. Transferring pretrained uni-modal models to multi-modal downstream tasks helps alleviate substantial computational costs for retraining multi-modal models. However, existing approaches primarily focus on multi-modal fusion, while neglecting the modal-specific fine-tuning, which is also crucial for multi-modal tasks. To this end, we propose parameter-efficient Collaborative Prompt Learning (CoPL) to fine-tune both uni-modal and multi-modal features. Specifically, the collaborative prompts consist of modal-specific prompts and modal-interaction prompts. The modal-specific prompts are tailored for fine-tuning each modality, while the modal-interaction prompts are customized to explore inter-modality association. Furthermore, prompt bank-based mutual coupling is introduced to extract instance-level features, further enhancing the model's generalization ability. Extensive experimental results demonstrate that our approach achieves comparable or higher performance on various audio-visual downstream tasks while utilizing approximately 1% extra trainable parameters.

Zhijun Jia, Huaying Xue, Xiulian Peng, Yan Lu 0001

Low resource of parallel data is the key challenge of accent conversion(AC) problem in which both the pronunciation units and prosody pattern need to be converted. We propose a two-stage generative framework "convert-and-speak" in which the conversion is only operated on the semantic token level and the speech is synthesized conditioned on the converted semantic token with a speech generative model in target accent domain. The decoupling design enables the "speaking" module to use massive amount of target accent speech and relieves the parallel data required for the "conversion" module. Conversion with the bridge of semantic token also relieves the requirement for the data with text transcriptions and unlocks the usage of language pre-training technology to further efficiently reduce the need of parallel accent speech data. To reduce the complexity and latency of "speaking", a single-stage AR generative model is designed to achieve good quality as well as lower computation cost. Experiments on Indian-English to general American-English conversion show that the proposed framework achieves state-of-the-art performance in accent similarity, speech quality, and speaker maintenance with only 15 minutes of weakly parallel data which is not constrained to the same speaker. Extensive experimentation with diverse accent types suggests that this framework possesses a high degree of adaptability, making it readily scalable to accommodate other accents with low-resource data. Audio samples are available at https://www.microsoft.com/en-us/research/project/convert-and-speak-zero-shot-accent-conversion-with-minimumsupervision/.

Taoyu Su, Jiawei Sheng, Shicheng Wang, Xinghua Zhang 0001, Hongbo Xu, Tingwen Liu

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where entities can be associated with related images. Most existing studies rely heavily on the automatically learned multi-modal fusion modules, which may allow redundant information such as misleading clues in the generated entity representations, impeding the feature consistency of equivalent entities. To this end, we propose a variational framework for MMEA via information bottleneck, termed as IBMEA, by emphasizing alignment-relevant information while suppressing alignment-irrelevant information in entity representations. Specifically, we first develop multi-modal variational encoders that represent modal-specific features as probability distributions. Then, we propose four modal-specific information bottleneck regularizers to limit the misleading clues in the modal-specific entity representations. Finally, we propose a modal-hybrid information contrastive regularizer to integrate modal-specific representations and ensure the similarity of equivalent entities between MMKGs to achieve MMEA. We conduct extensive experiments on 2 cross-KG and 3 bilingual MMEA datasets. Experimental results demonstrate that our model consistently outperforms previous state-of-the-art methods, and also shows promising and robust performance especially in the low-resource and high-noise data scenarios.

Qiuhui Chen, Yi Hong

Multimodal medical data, such as brain scans and non-imaging clinical records like demographics and neuropsychology examinations, play an important role in diagnosing neurodegenerative disorders, e.g., Alzheimer's disease (AD) and Parkinson's disease (PD). However, the disease-relevant information is overwhelmed by the high-dimensional image scans and the massive non-imaging data, making it a challenging task to fuse multimodal medical inputs efficiently. Recent multimodal learning methods adopt deep encoders to extract features and simple concatenation or alignment techniques for feature fusion, which suffer the representation degeneration issue due to the vast irrelevant information. To address this challenge, we propose a deep self-weighted multimodal relevance weighting approach, which leverages clustering-based constrastive learning and eliminates the intra- and inter-modal irrelevancy. The learned relevance score is integrated as a gate with a multimodal attention transformer to provide an improved fusion for the final diagnosis. Our proposed model, called SMART (Self-weighted Multimodal Attention-and-Relevance gated Transformer), is extensively evaluated on three public AD/PD datasets and achieves state-of-the-art (SOTA) performance in the diagnostics of neurodegenerative disorders. Our source code is available at https://github.com/Qybc/SMART.

Muquan Li, Dongyang Zhang 0001, Tao He 0007, Xiurui Xie, Yuan-Fang Li, Ke Qin

Data-free knowledge distillation (DFKD) has emerged as a pivotal technique in the domain of model compression, substantially reducing the dependency on the original training data. Nonetheless, conventional DFKD methods that employ synthesized training data are prone to the limitations of inadequate diversity and discrepancies in distribution between the synthesized and original datasets. To address these challenges, this paper introduces an innovative approach to DFKD through diverse diffusion augmentation (DDA). Specifically, we revise the paradigm of common data synthesis in DFKD to a composite process through leveraging diffusion models subsequent to data synthesis for self-supervised augmentation, which generates a spectrum of data samples with similar distributions while retaining controlled variations. Furthermore, to mitigate excessive deviation in the embedding space, we introduce an image filtering technique grounded in cosine similarity to maintain fidelity during the knowledge distillation process. Comprehensive experiments conducted on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets showcase the superior performance of our method across various teacher-student network configurations, outperforming the contemporary state-of-the-art DFKD methods. Code will be available at: https://github.com/SLGSP/DDA.

Mengmeng Sheng, Zeren Sun, Gensheng Pei, Tao Chen 0012, Haonan Luo 0002, Yazhou Yao

Label noise, an inevitable issue in various real-world datasets, tends to impair the performance of deep neural networks. A large body of literature focuses on symmetric co-training, aiming to enhance model robustness by exploiting interactions between models with distinct capabilities. However, the symmetric training processes employed in existing methods often culminate in model consensus, diminishing their efficacy in handling noisy labels. To this end, we propose an Asymmetric Co-Training (ACT) method to mitigate the detrimental effects of label noise. Specifically, we introduce an asymmetric training framework in which one model (i.e., RTM) is robustly trained with a selected subset of clean samples while the other (i.e., NTM) is conventionally trained using the entire training set. We propose two novel criteria based on agreement and discrepancy between models, establishing asymmetric sample selection and mining. Moreover, a metric, derived from the divergence between models, is devised to quantify label memorization, guiding our method in determining the optimal stopping point for sample mining. Finally, we propose to dynamically re-weight identified clean samples according to their reliability inferred from historical information. We additionally employ consistency regularization to achieve further performance improvement. Extensive experimental results on synthetic and real-world datasets demonstrate the effectiveness and superiority of our method.

Nan Wang 0027, Zonglin Di, Houlin He, Qingchao Jiang, Xiaoxiao Li

Deep learning for medical image classification needs large amounts of carefully labeled data with the aid of domain experts. However, data labeling is vulnerable to noises, which may degrade the accuracy of classifiers. Given the cost of medical data collection and annotation, it is highly desirable for methods that can effectively utilize noisy labeled data. In addition, efficiency and universality are essential for noisy label training, which requires further research.To address the lack of high-quality labeled medical data and meet algorithm efficiency requirements for clinical application, we propose a simple yet effective approach for multi-field medical images to utilize noisy data, named Pseudo-T correction. Specifically, we design a noisy label filter to divide the training data into clean and noisy samples. Then, we estimate a transition matrix that corrects model predictions based on the partitions of clean and noisy data samples. However, if the model overfits noisy data, noisy samples become more difficult to detect in the filtering step, resulting in inaccurate transition matrix estimation. Therefore, we employ gradient disparity as an effective criterion to decide whether or not to refine the transition matrix in the model's further training steps. The novel design enables us to build more accurate machine-learning models by leveraging noisy labels. We demonstrate that our method outperforms the state-of-the-art methods on three public medical datasets and achieves superior computational efficiency over the alternatives.

Yi Zhang 0109, Ke Yu 0004, Angelica I. Avilés-Rivero, Jiyuan Jia, Yushun Tang, Zhihai He

In this paper, we address the challenge of adapting vision-language models (VLMs) to few-shot image recognition in a training-free manner. We observe that existing methods are not able to effectively characterize the semantic relationship between support and query samples in a training-free setting. We recognize that, in the semantic feature space, the feature of the query image is a linear and sparse combination of support image features since support-query pairs are from the class and share the same small set of distinctive visual attributes. Motivated by this interesting observation, we propose a novel method called Training-free Feature ReConstruction with Sparse optimization (TaCo), which formulates the few-shot image recognition task as a feature reconstruction and sparse optimization problem. Specifically, we exploit the VLM to encode the query and support images into features. We utilize sparse optimization to reconstruct the query feature from the corresponding support features. The feature reconstruction error is then used to define the reconstruction similarity. Coupled with the text-image similarity provided by the VLM, our reconstruction similarity analysis accurately characterizes the relationship between support and query images. This results in significantly improved performance in few-shot image recognition. Our extensive experimental results on few-shot recognition demonstrate that our method outperforms existing state-of-the-art approaches by substantial margins.