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Zhangtao Cheng, Jiao Li, Jian Lang, Ting Zhong, Fan Zhou

Micro-video popularity prediction (MVPP) plays a crucial role in various downstream applications. Recently, multimodal methods that integrate multiple modalities to predict the popularity have exhibited impressive performance. However, these methods face several unresolved issues: (1) limited contextual information and (2) incomplete modal semantics. Incorporating relevant videos and performing full fine-tuning on pre-trained models typically achieves powerful capabilities in addressing these issues. However, this paradigm is not optimal due to its weak transferability and scarce downstream data. Inspired by prompt learning, we propose ICPF, a novel In-Context Prompt-augmented Framework to enhance popularity prediction. ICPF maintains a model-agnostic design, facilitating seamless integration with various multimodal fusion models. Specifically, the multi-branch retriever first retrieves similar modal content through within-modality similarities. Next, in-context prompt generator extracts semantic prior features from retrieved videos and generates in-context prompts, enriching pre-trained models with valuable contextual knowledge. Finally, knowledge-augmented predictor captures complementary features including modal semantics and popularity information. Extensive experiments conducted on three real-world datasets demonstrate the superiority of ICPF compared to 14 competitive baselines.

Kexin Zheng, Haifeng Xia, Siyu Xia, Ming Shao, Zhengming Ding

Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods.

Yi Zhang, Chun-Wun Cheng, Junyi He, Zhihai He, Carola-Bibiane Schönlieb, Yuyan Chen, Angelica I Aviles-Rivero

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP’s robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance.

Ximin Zhang, Jinyin Chen, Haibin Zheng, Zhenguang Liu

Deep neural networks (DNNs) have achieved remarkable success in widespread applications. Meanwhile, its vulnerability towards carefully crafted adversarial attacks captures special attention. Not only adversarial perturbations in digital space will fool the target DNNs-based detectors making a wrong decision, but also actually printed patches can be camouflaged to defeat detectors in physical space. In particular, multi-view physical adversarial attacks pose a more serious threat to practical scenarios. The existing attacks are still challenged in three aspects, i.e., high-cost data augmentation, attack performance gap between digital and physical space, and low attack transferability across DNNs. To overcome the challenges, we introduce PhyCamo, a robust physical camouflage framework based on contrastive learning that distinguishes itself from prior research in various critical ways: (1) data augmentation - it utilizes the diffusion model for data augmentation to efficiently simulate sophisticated physical dynamics in real-world; (2) robustness - it leverages contrastive learning to optimize physical camouflage against encoders with the state-of-the-art (SOTA) attack performance; (3) transferability - it mitigates the model-specific noise in the optimization by adopting diverse input methods, thereby amplifying the transferability between models. Extensive experiments are carried out on a car dataset, a tank dataset, and a pedestrian dataset, comparing with 6 classic multi-view physical adversarial attacks in both digital and physical spaces. The results demonstrate PhyCamo’s superior performance. For instance, it generates more effective physical camouflage (with higher attack success rate~×1.26 and reduce the model's average precision by 55%). PhyCamo can also help to improve the robustness of detectors through adversarial training, which contributes to the application of deep neural networks in the field of security sensitivity.

Mingjin Zhang, Wenteng Shang, Fei Gao, Qiming Zhang, FengQin Lu, Jing Zhang

Single-frame Infrared Small Target (SIRST) detection has made significant advancements, but it still faces challenges due to limited labeled data and the foreground-background class imbalance. To address these issues, we introduce a novel Semi-Supervised SIRST Detection (S^3D) pipeline in this paper. First, drawing inspiration from thermodynamics, we propose augmenting infrared images using both chromatically and spatially uneven perturbations. This dual-stream perturbation enhances the diversity and balance of infrared samples, contributing to the robustness of detection models. Additionally, we develop a confidence-adaptive matching method to maintain weighted consistency among perturbed unlabeled samples. Second, to tackle class imbalance in labeled data, we compel the model to generate discriminative predictions for challenging, misclassified examples while down-weighting well-classified examples. We achieve this by modifying the standard cross-entropy loss to squeeze the detector and truncating the loss on well-classified examples. Our innovative Truncated Squeeze (TS) loss focuses on learning discriminative representations for difficult cases and prevents over-optimization for simpler ones. To assess the effectiveness of the perturbation techniques and loss functions, we apply them to various SIRST detectors and conduct comprehensive experiments on two benchmark datasets. Notably, our proposed methods consistently and significantly improve accuracy. Remarkably, our approach achieves over 98% performance of the state-of-the-art fully-supervised method using only 1/8 of the labeled samples.

Yi Yu, Song Xia, Xun Lin, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex Kot

No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and optimizing computer vision systems, e.g., low-light enhancement. Recent research indicates that NR-IQA models are susceptible to adversarial attacks, which can significantly alter predicted scores with visually imperceptible perturbations. Despite revealing vulnerabilities, these attack methods have limitations, including high computational demands, untargeted manipulation, limited practical utility in white-box scenarios, and reduced effectiveness in black-box scenarios. To address these challenges, we shift our focus to another significant threat and present a novel poisoning-based backdoor attack against NR-IQA (BAIQA), allowing the attacker to manipulate the IQA model's output to any desired target value by simply adjusting a scaling coefficient alpha for the trigger. We propose to inject the trigger in the discrete cosine transform (DCT) domain to improve the local invariance of the trigger for countering trigger diminishment in NR-IQA models due to widely adopted data augmentations. Furthermore, the universal adversarial perturbations (UAP) in the DCT space are designed as the trigger, to increase IQA model susceptibility to manipulation and improve attack effectiveness. In addition to the heuristic method for poison-label BAIQA (P-BAIQA), we explore the design of clean-label BAIQA (C-BAIQA), focusing on alpha sampling and image data refinement, driven by theoretical insights we reveal. Extensive experiments on diverse datasets and various NR-IQA models demonstrate the effectiveness of our attacks.

Qing Yu, Mikihiro Tanaka, Kent Fujiwara

Generation of 3D human motion holds significant importance in the creative industry. While recent notable advances have been made in generating common motions, existing methods struggle to generate diverse and rare motions due to the complexity of motions and limited training data. This work introduces ReMoGPT, a unified motion-language generative model that solves a wide range of motion-related tasks by incorporating a multi-modal retrieval mechanism into the generation process to address the limitations of existing models, namely diversity and generalizability. We propose to focus on body-part-level motion features to enable fine-grained text-motion retrieval and locate suitable references from the database to conduct generation. Then, the motion-language generative model is trained with prompt-based question-and-answer tasks designed for different motion-relevant problems. We incorporate the retrieved samples into the prompt, and then perform instruction tuning of the motion-language model, to learn from task feedback and produce promising results with the help of fine-grained multi-modal retrieval. Extensive experiments validate the efficacy of ReMoGPT, showcasing its superiority over existing state-of-the-art methods. The framework performs well on multiple motion tasks, including motion retrieval, generation, and captioning.

Zejun Yang, Huawei Wei, Zhisheng Wang

We introduce RealPortrait, a framework based on Diffusion Transformers (DiT), designed to generate highly expressive and visually appealing portrait animations. Given a static portrait image, our method can transfer complex facial expressions and head pose movements extracted from a driving video onto the portrait, transforming it into a lifelike video. Specifically, we exploit the robust spatial-temporal modeling capabilities of DiT, enabling the generation of portrait videos that maintain high-fidelity visual details and ensure temporal coherence. In contrast to conventional image-to-video generation frameworks that necessitate a separate reference network, we incorporate an efficient reference attention within the DiT backbone, thereby obviating the computational overhead and achieving superior reference appearance preservation. Concurrently, we integrate a parallel ControlNet to precisely regulate intricate facial expressions and head poses. Diverging from prior methods that utilize explicit sparse motion representations, such as facial landmarks or 3DMM coefficients, we adopt a dense implicit motion representation as the control guidance. This implicit motion representation excels in capturing nuanced emotional facial expressions and subtle non-rigid dynamics of the lips. To further enhance the generalization capability of the model, we augment the training dataset by incorporating a substantial volume of facial image data through random crop augmentation. This strategy ensures the model's robustness across a wide variety of facial appearances and expressions. Empirical evaluations demonstrate that RealPortrait excels in generating portrait animations with highly-realistic quality and exceptional temporal coherence in appearance retention.

Jiarui Yang, Chuan Wang, Jun Zhang, Shuyi Wu, Jinjing Zhao, Zeming Liu, Liang Yang

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model's robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG.

Cheng Xu, Weiwen Zhang, Hongrui Zhang, Xuemiao Xu, Huaidong Zhang, Jing Zou, Jing Qin

In clinical imaging, medical segmentation networks typically require continually adapting to new data from multiple sites over time, as aggregating all data for learning at once can be impractical due to storage limitations and privacy concerns. However, existing methods basically overlook domain-specific characteristics and fall short of adequately capturing domain-invariant knowledge during continual learning, leading to undesired catastrophic forgetting of previous sites and inferior generalization to new sites. To tackle this issue, this paper introduces FR2Seg, to sufficiently exploit both domain-specific and domain-invariant knowledge for efficient continual learning with the aid of low-frequency cues. For the former aspect, we propose a Fourier style replay module to synthesize pseudo images with old-site styles for data augmentation during new-site training, effectively preventing catastrophic forgetting without sacrificing data privacy. For the latter, we present a Fourier adaptive consistency regularization to identify and constrain the optimization of domain-invariant parameters with explicit awareness of knowledge transferability across sites, ensuring excellent generalizability to new sites. Experimental results on two public datasets confirm our method's superiority over existing state-of-the-art continual learning methods.

Yang Wei, Jingyu Tan, Guowen Xu, Zhuoran Ma, Zhuo Ma, Bin Xiao

Substitute training-based data-free black-box attacks pose a significant threat to enterprise-deployed models. These attacks use a generator to synthesize data and query APIs, then train a substitute model to approximate the target model's decision boundary based on the returned results. However, existing attack methods often struggle to produce sufficiently diverse data, particularly for complex target models and extensive target data domains, severely limiting their practical application. To address this gap, we design domain-augmented learning to improve the quality of the synthetic data domain (SDD) generated by the generator from two perspectives. Specifically, (1) To broaden the SDD's coverage, we introduce textual semantic embeddings into the generator for the first time. (2) For enhancing the SDD's discretization, we propose a competitive optimization strategy that forces the generator to self-compete, along with heterogeneity excitation to overcome the constraints of information entropy on diversity. Comprehensive experiments demonstrate that our method is more effective. In non-targeted attacks on the CIFAR-10 and Tiny-ImageNet datasets, our method outperforms the state-of-the-art by 14% and 7% in attack success rate, respectively.

Zhiming Wang, Lin Gu, Feng Lu

CutMix is a data augmentation strategy that cuts and pastes image patches to mixup training data. Existing methods pick either random or salient areas which are often inconsistent to labels, thus misguiding the training model. By our knowledge, we integrate human gaze to guide cutmix for the first time. Since human attention is driven by both high-level recognition and low-level clues, we propose a controllable Top-down Attention Guided Module to obtain a general artificial attention which balances top-down and bottom-up attention. The proposed TdATttenMix then picks the patches and adjust the label mixing ratio that focuses on regions relevant to the current label. Experimental results demonstrate that our TdAttenMix outperforms existing state-of-the-art mixup methods across eight different benchmarks. Additionally, we introduce a new metric based on the human gaze and use this metric to investigate the issue of image-label inconsistency.

Yun Wang, Jiahao Zheng, Chenghao Zhang, Zhanjie Zhang, Kunhong Li, Yongjian Zhang, Junjie Hu

Self-supervised stereo matching has drawn attention due to its ability to estimate disparity without needing ground-truth data. However, existing self-supervised stereo matching methods heavily rely on the photo-metric consistency assumption, which is vulnerable to natural disturbances, resulting in ambiguous supervision and inferior performance compared to the supervised ones. To relax the limitation of the photo-metric consistency assumption and even bypass this assumption, we propose a novel self-supervised framework named DualNet, which consists of two key steps: robust self-supervised teacher learning and pseudo-label supervised student training. Specifically, the teacher model is first trained in a self-supervised manner with a focus on feature-metric consistency and data augmentation consistency. Then, the output of the teacher model is geometrically constrained to obtain high-quality pseudo labels. Benefiting from these high-quality pseudo labels, the student model can outperform its teacher model by a large margin. With the two well-designed steps, the proposed framework DualNet ranks 1st among all self-supervised methods on multiple benchmarks, surprisingly even outperforming several supervised counterparts.

Xinyi Wang, Na Zhao, Zhiyuan Han, Dan Guo, Xun Yang

3D visual grounding (3DVG), which aims to correlate a natural language description with the target object within a 3D scene, is a significant yet challenging task. Despite recent advancements in this domain, existing approaches commonly encounter a shortage: a limited amount and diversity of text-3D pairs available for training. Moreover, they fall short in effectively leveraging different contextual clues (e.g., rich spatial relations within the 3D visual space) for grounding. To address these limitations, we propose AugRefer, a novel approach for advancing 3D visual grounding. AugRefer introduces cross-modal augmentation designed to extensively generate diverse text-3D pairs by placing objects into 3D scenes and creating accurate and semantically rich descriptions using foundation models. Notably, the resulting pairs can be utilized by any existing 3DVG methods for enriching their training data. Besides, AugRefer presents a language-spatial adaptive decoder that effectively adapts the potential referring objects based on the language description and various 3D spatial relations. Extensive experiments on three benchmark datasets clearly validate the effectiveness of AugRefer.

Zhihao Tang, Xi Zhang, Chaozhuo Li

Deep learning has significantly enhanced survival prediction using whole slide images (WSIs) by adopting a two-stage learning paradigm: WSI preparation and patient-level prediction. While existing research generally concentrates on developing advanced patient-level prediction modules, the critical importance of WSI preparation has been largely overlooked. In practice, WSI preparation is influenced by numerous factors, including tissue heterogeneity, sampling strategies, and technical considerations. These uncontrollable external factors incur variability in the number of WSIs among patients, introducing significant bias and resulting in inferior performance for patients with few WSIs. To address this challenge, we propose a novel approach named WSI-Diffusion. Unlike existing WSI generation models that produce augmented versions of input WSIs, our method generates entirely new WSIs in representation space to serve as complementary data. WSIDiffusion employs a two-stage hierarchical diffusion process. Two novel modules, WSI-level and patch-level Diffusers are designed to capture complex correlations between WSIs and patches. The generated WSIs are integrated as supplementary data, and a light patient-level prediction module is then trained for survival prediction. Experimental results across five datasets demonstrate the superiority of our proposal.

Kunming Tang, Zhiguo Jiang, Jun Shi, Wei Wang, Haibo Wu, Yushan Zheng

Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods.

Jingyuan Sun, Mingxiao Li, Marie-Francine Moens

In our quest to decode the visual processing of the human brain, we aim to reconstruct dynamic visual experiences from brain activities, a task both challenging and intriguing. Although recent advances have made significant strides in reconstructing static images from non-invasive brain recordings, the translation of continuous brain activities into video formats has not been extensively explored. Our study introduces NeuralFlix, a simple but effective dual-phase framework designed to address the inherent challenges in decoding fMRI data, such as noise, spatial redundancy, and temporal lags. The framework employs spatial and temporal augmentation for contrastive learning of fMRI representations, and a diffusion model enhanced with dependent prior noise for generating videos. Tested on a publicly available fMRI dataset, NeuralFlix demonstrates promising results, significantly outperforming previous state-of-the-art models by margins of 20.97%, 31.00%, and 12.30%, respectively, in decoding the brain activities of three subjects individually, as measured by SSIM.

Yaling Shen, Zhixiong Zhuang, Kun Yuan, Maria-Irina Nicolae, Nassir Navab, Nicolas Padoy, Mario Fritz

Medical multimodal large language models (MLLMs) are becoming an instrumental part of healthcare systems, assisting medical personnel with decision making and results analysis. Models for radiology report generation are able to interpret medical imagery, thus reducing the workload of radiologists. As medical data is scarce and protected by privacy regulations, medical MLLMs represent valuable intellectual property. However, these assets are potentially vulnerable to model stealing, where attackers aim to replicate their functionality via black-box access. So far, model stealing for the medical domain has focused on image classification; however, existing attacks are not effective against MLLMs. In this paper, we introduce Adversarial Domain Alignment (ADA-Steal), the first stealing attack against medical MLLMs. ADA-Steal relies on natural images, which are public and widely available, as opposed to their medical counterparts. We show that data augmentation with adversarial noise is sufficient to overcome the data distribution gap between natural images and the domain-specific distribution of the victim MLLM. Experiments on the IU X-RAY and MIMIC-CXR radiology datasets demonstrate that Adversarial Domain Alignment enables attackers to steal the medical MLLM without any access to medical data.

Miao Rang, Zhenni Bi, Chuanjian Liu, Yehui Tang, Kai Han, Yunhe Wang

Multimodal vision language models (VLMs) have made significant progress with the support of continuously increasing model sizes and data volumes. Running VLMs on edge devices has become a challenge for their widespread application. There are several efficient VLM efforts, but they often sacrifice linguistic capabilities to enhance multimodal abilities, or require extensive training. To address this quandary, we introduce the innovative framework of Efficient Vision Language Models with Elastic Visual Experts (Eve). By strategically incorporating adaptable visual expertise at multiple stages of training, Eve strikes a balance between preserving linguistic abilities and augmenting multimodal capabilities. This balanced approach results in a versatile model with only 1.8B parameters that delivers significant improvements in both multimodal and linguistic tasks. Notably, in configurations below 3B parameters, Eve distinctly outperforms in language benchmarks and achieves state-of-the-art results in VLM Benchmarks. Additionally, its multimodal accuracy outstrips that of the larger 7B LLaVA-1.5 model.

Wentian Qu, Jiahe Li, Jian Cheng, Jian Shi, Chenyu Meng, Cuixia Ma, Hongan Wang, Xiaoming Deng, Yinda Zhang

Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. However, due to significant occlusions between hands and object as well as the high degree-of-freedom motions, it is challenging to collect and annotate a high-quality, large-scale dataset, which prevents further improvement of bimanual hand-object interaction-related baselines. In this work, we propose a new 3D Gaussian Splatting based data augmentation framework for bimanual hand-object interaction, which is capable of augmenting existing dataset to large-scale photorealistic data with various hand-object pose and viewpoints. First, we use mesh-based 3DGS to model objects and hands, and to deal with the rendering blur problem due to multi-resolution input images used, we design a super-resolution module. Second, we extend the single hand grasping pose optimization module for the bimanual hand object to generate various poses of bimanual hand-object interaction, which can significantly expand the pose distribution of the dataset. Third, we conduct an analysis for the impact of different aspects of the proposed data augmentation on the understanding of the bimanual hand-object interaction. We perform our data augmentation on two benchmarks, H2O and Arctic, and verify that our method can improve the performance of the baselines.