Existing cross-domain transferable attacks mostly focus on exploring the adversarial transferability across homomodal domains, while the adversarial transferability across heteromodal domains, e.g., image domains to video domains, has received less attention. This paper investigates cross-modal transferable attacks from image domains to video domains with the generator-oriented approach, i.e., crafting adversarial perturbations for each frame of video clips with the perturbation generator trained in the ImageNet domain to attack target video models. To this end, we propose an effective Generative Cross-Modal Attacks (GCMA) framework to enhance adversarial transferability from image domains to video domains. To narrow the domain gap between image and video data, we first propose a random motion module that warps images with synthetic random optical flows. We then integrate the random motion module into the feature disruption loss to incorporate additional temporal cues in the training phase. Specifically, feature disruption loss minimizes the cosine similarity between intermediate features of warped benign and adversarial images. Furthermore, motivated by the positive correlation between transferability and temporal consistency of adversarial video clips, we also introduce a temporal consistency loss that maximizes the cosine similarity between intermediate features of warped adversarial images and adversarial counterparts of warped benign images. Finally, GCMA trains the perturbation generator by simultaneously optimizing feature disruption loss and temporal consistency loss. Extensive experiments demonstrate the effectiveness of our proposed method, achieving state-of-the-art performance on Kinetics-400 and UCF-101. Our code is available at https://github.com/kay-ck/GCMA.
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Due to the lack of spatial cues giving merely a single image, many monocular depth estimation methods have been developed to leverage stereo or multi-view images to learn the spatial information of a scene in a self-supervised manner. However, these methods have limited performance gain since they are not able to exploit sufficient 3D geometry cues during inference, where only monocular images are available. In this work, we present V2Depth, a novel coarse-to-fine framework with Virtual View feature simulation for supervised monocular Depth estimation. Specifically, we first design a virtual-view feature simulator by leveraging the technique of novel view synthesis and contrastive learning to generate virtual view feature maps. In this way, we explicitly provide representative spatial geometry for subsequent depth estimation in both the training and inference stages. Then we introduce a 3DVA-Refiner to iteratively optimize the predicted depth map. During the optimization process, 3D-aware virtual attention is developed to capture the global spatial-context correlations to maintain the feature consistency of different views and estimation integrity of the 3D scene such as objects with occlusion relationships. Decisive improvements over state-of-the-art approaches on three benchmark datasets across all metrics demonstrate the superiority of our method.
Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images. In detail, multiple HR images may produce similar LR versions due to complex blind degradations, causing the HR-dependent only codebooks having limited texture diversity when faced with confusing LR inputs. To alleviate this problem, we propose the Rich Texture-aware Codebook-based Network (RTCNet), which consists of the Degradation-robust Texture Prior Module (DTPM) and the Patch-aware Texture Prior Module (PTPM). DTPM effectively mines the cross-resolution correlation of textures between LR and HR images by exploiting the cross-resolution correspondence of textures. PTPM uses patch-wise semantic pre-training to correct the misperception of texture similarity in the high-level semantic regularization. By taking advantage of this, RTCNet effectively gets rid of the misalignment of confusing textures between HR and LR in the BSR scenarios. Experiments show that RTCNet outperforms state-of-the-art methods on various benchmarks by up to 0.16 ~ 0.46dB.
Backdoor attacks for point clouds have elicited mounting interest with the proliferation of deep learning. The point cloud classifiers can be vulnerable to malicious actors who seek to manipulate or fool the model with specific backdoor triggers. Detecting and rejecting backdoor samples during the inference stage can effectively alleviate backdoor attacks. Recently, some black-box test-time backdoor sample detection methods have been proposed in the 2D image domain, without any underlying assumptions about the backdoor triggers. However, upon examination, we have found that these detection techniques are not effective for 3D point clouds. As a result, there is a pressing need to bridge the gap for the development of a universal approach that is specifically designed for 3D point clouds. In this paper, we propose the first test-time backdoor sample detection method in 3D point cloud without assumption to the backdoor triggers, called Point Clouds Corruption Robustness Test (PointCRT). Based on the fact that the corruption robustness of clean samples remains relatively stable across various backdoor models, we propose the corruption robustness score to map the features into high-dimensional space. The corruption robustness score is a vector evaluated by label consistency, whose element is the minimum severity level of corruption that changes the label prediction of the victim model. Then, the trigger is identified by detecting the abnormal corruption robustness score through a nonlinear classification. The comprehensive experiments demonstrate PointCRT deals with all cases with the average AUC over 0.934 and F1 score over 0.864, with the enhancement of 18%-28% on ModelNet40. Our codes are available at: https://github.com/CGCL-codes/PointCRT.
Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian. Recent methods often address the PAR problem by training a multi-label classifier with predefined attribute classes, but they can hardly exhaust all possible pedestrian attributes in the real world. To tackle this problem, we propose a novel Pedestrian Open-Attribute Recognition (POAR) approach by formulating the problem as a task of image-text search. Our approach employs a Transformer-based Encoder with a Masking Strategy (TEMS) to focus on the attributes of specific pedestrian parts (e.g., head, upper body, lower body, feet, etc.), and introduces a set of attribute tokens to encode the corresponding attributes into visual embeddings. Each attribute category is described as a natural language sentence and encoded by the text encoder. Then, we compute the similarity between the visual and text embeddings to find the best attribute descriptions for the input images. To handle multiple attributes of a single pedestrian, we propose a Many-To-Many Contrastive (MTMC) loss with masked tokens. In addition, we propose a Grouped Knowledge Distillation (GKD) method to minimize the disparity between visual embeddings and unseen attribute text embeddings. We evaluate our proposed method on three PAR datasets with an open-attribute setting. The results demonstrate the effectiveness of our method as a strong baseline for the POAR task. Our code is available at https://github.com/IvyYZ/POAR.
In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial relation understanding and numeration failure) in complex natural scenes, which impedes the high-faithfulness text-to-image generation. Although recent efforts have been made to improve controllability by giving fine-grained guidance (e.g., sketch and scribbles), this issue has not been fundamentally tackled since users have to provide such guidance information manually. In this work, we strive to synthesize high-fidelity images that are semantically aligned with a given textual prompt without any guidance. Toward this end, we propose a coarse-to-fine paradigm to achieve layout planning and image generation. Concretely, we first generate the coarse-grained layout conditioned on a given textual prompt via in-context learning based on Large Language Models. Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art models in terms of layout and image generation. Our code and settings are available at https://layoutllm-t2i.github.io/.
The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists an indelible domain gap between the datasets utilized for training (i.e., natural images) and those utilized for inference (e.g., artistic images). Our proposal involves a low-rank adaptation approach aimed at addressing the rate-distortion drop observed in out-of-domain datasets. Specifically, we perform low-rank matrix decomposition to update certain adaptation parameters of the client's decoder. These updated parameters, along with image latents, are encoded into a bitstream and transmitted to the decoder in practical scenarios. Due to the low-rank constraint imposed on the adaptation parameters, the resulting bit rate overhead is small. Furthermore, the bit rate allocation of low-rank adaptation is non-trivial, considering the diverse inputs require varying adaptation bitstreams. We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation. The dynamic adaptation network is optimized end-to-end using rate-distortion loss. Our proposed method exhibits universality across diverse image datasets. Extensive results demonstrate that this paradigm significantly mitigates the domain gap, surpassing non-adaptive methods with an average BD-rate improvement of approximately 19% across out-of-domain images. Furthermore, it outperforms the most advanced instance adaptive methods by roughly 5% BD-rate. Ablation studies confirm our method's ability to universally enhance various image compression architectures. Our project is available at https://github.com/llvy21/DUIC.
The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey meaning, humor, and cultural significance. While meme analysis has been an active area of research, little work has been done on unsupervised multimodal topic modeling of memes, which is important for content moderation, social media analysis, and cultural studies. We propose PromptMTopic, a novel multimodal prompt-based model designed to learn topics from both text and visual modalities by leveraging the language modeling capabilities of large language models. Our model effectively extracts and clusters topics learned from memes, considering the semantic interaction between the text and visual modalities. We evaluate our proposed model through extensive experiments on three real-world meme datasets, which demonstrate its superiority over state-of-the-art topic modeling baselines in learning descriptive topics in memes. Additionally, our qualitative analysis shows that PromptMTopic can identify meaningful and culturally relevant topics from memes. Our work contributes to the understanding of the topics and themes of memes, a crucial form of communication in today's society. Disclaimer: This paper contains sensitive content that may be disturbing to some readers.
This paper presents a prior instruction representation framework (PIR) for remote sensing image-text retrieval, aimed at remote sensing vision-language understanding tasks to solve the semantic noise problem. Our highlight is the proposal of a paradigm that draws on prior knowledge to instruct adaptive learning of vision and text representations. Concretely, two progressive attention encoder (PAE) structures, Spatial-PAE and Temporal-PAE, are proposed to perform long-range dependency modeling to enhance key feature representation. In vision representation, Vision Instruction Representation (VIR) based on Spatial-PAE exploits the prior-guided knowledge of the remote sensing scene recognition by building a belief matrix to select key features for reducing the impact of semantic noise. In text representation, Language Cycle Attention (LCA) based on Temporal-PAE uses the previous time step to cyclically activate the current time step to enhance text representation capability. A cluster-wise affiliation loss is proposed to constrain the inter-classes and to reduce the semantic confusion zones in the common subspace. Comprehensive experiments demonstrate that using prior knowledge instruction could enhance vision and text representations and could outperform the state-of-the-art methods on two benchmark datasets, RSICD and RSITMD. Codes are available at https://github.com/Zjut-MultimediaPlus/PIR-pytorch.
Keyword spotting (KWS) presents a formidable challenge, particularly in high-noise environments. Traditional denoising algorithms that rely solely on speech have difficulty recovering speech that has been severely corrupted by noise. In this investigation, we develop an adaptive text-informed denoising model to bolster reliable keyword identification in the presence of considerable noise degradation. The whole proposed TE-KWS incorporates a tripartite branch structure, where the speech branch (SB) takes noisy speech as input which provides the raw speech information, the alignment branch (AB) accommodates aligned text input which facilitates accurate restoration of the corresponding speech when text with alignment is preserved, and the text branch (TB) handles unaligned text which prompts the model to autonomously learn the alignment between speech and text. To make the proposed denoising model more beneficial for KWS, following the training of the whole model,the alignment branch (AB) is frozen, and the model is fine-tuned by leveraging its speech restoration and forced alignment capabilities. Subsequently, the input for the text branch (TB) is supplanted with designated keywords, and a heavier denoising penalty is applied on the keywords period, thereby explicitly intensifying the speech restoration ability of the model for keywords. Finally, the Combined Adversarial Domain Adaptation (CADA) is implemented to enhance the robustness of KWS with regard to data pre-and post-speech enhancement (SE). Experimental results indicate that our approach not only markedly ameliorates highly corrupted speech, achieving SOTA performance for marginally corrupted speech, but also bolsters the efficacy and generalizability of prevailing mainstream KWS models.
Emotional video captioning (EVC) is an emerging task to describe the factual content with the inherent emotion expressed in a video. It is crucial for the EVC task to effectively perceive subtle and ambiguous visual emotion cues in the stage of caption generation. However, existing captioning methods usually overlooked the learning of emotions in user-generated videos, thus making the generated sentence a bit boring and soulless. To address this issue, this paper proposes a new emotional captioning perspective in a human-like perception-priority manner, i.e., first perceiving the inherent emotion and then leveraging the perceived emotion cue to support caption generation. Specifically, we devise an Emotion-Prior Awareness Network (EPAN). It mainly benefits from a novel tree-structured emotion learning module involving both catalog-level psychological categories and lexical-level usual words to achieve the goal of explicit and fine-grained emotion perception. Besides, we develop a novel subordinate emotion masking mechanism between the catalog level and lexical level that facilitates coarse-to-fine emotion learning. Afterward, with the emotion prior, we can effectively decode the emotional caption by exploiting the complementation of visual, textual, and emotional semantics. In addition, we also introduce three simple yet effective optimization objectives, which can significantly boost the emotion learning from the perspectives of emotional captioning, hierarchical emotion classification, and emotional contrastive learning. Sufficient experimental results on three benchmark datasets clearly demonstrate the advantages of our proposed EPAN over existing SOTA methods in both semantic and emotional metrics. The extensive ablation study and visualization analysis further reveal the good interpretability of our emotional video captioning method. Code will be made available at https://github.com/songpipi/EPAN.
Billions of people are sharing their daily live images on social media everyday. However, malicious collectors use deep face recognition systems to easily steal their biometric information (e.g., faces) from these images. Some studies are being conducted to generate encrypted face photos using adversarial attacks by introducing imperceptible perturbations to reduce face information leakage. However, existing studies need stronger black-box scenario feasibility and more natural visual appearances, which challenge the feasibility of privacy protection. To address these problems, we propose a frequency-restricted identity-agnostic (FRIA) framework to encrypt face images from unauthorized face recognition without access to personal information. As for the weak black-box scenario feasibility, we obverse that representations of the average feature in multiple face recognition models are similar, thus we propose to utilize the average feature via the crawled dataset from the Internet as the target to guide the generation, which is also agnostic to identities of unknown face recognition systems; in nature, the low-frequency perturbations are more visually perceptible by the human vision system. Inspired by this, we restrict the perturbation in the low-frequency facial regions by discrete cosine transform to achieve the visual naturalness guarantee. Extensive experiments on several face recognition models demonstrate that our FRIA outperforms other state-of-the-art methods in generating more natural encrypted faces while attaining high black-box attack success rates of 96%. In addition, we validate the efficacy of FRIA using real-world black-box commercial API, which reveals the potential of FRIA in practice. Our codes can be found in https://github.com/XinDong10/FRIA.
Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at https://github.com/Qrange-group/SUR-adapter.
Recent advancements in video-language understanding have been established on the foundation of image-text models, resulting in promising outcomes due to the shared knowledge between images and videos. However, video-language understanding presents unique challenges due to the inclusion of highly complex semantic details, which result in information redundancy, temporal dependency, and scene complexity. Current techniques have only partially tackled these issues, and our quantitative analysis indicates that some of these methods are complementary. In light of this, we propose a novel framework called RTQ (Refine, Temporal model, and Query), which addresses these challenges simultaneously. The approach involves refining redundant information within frames, modeling temporal relations among frames, and querying task-specific information from the videos. Remarkably, our model demonstrates outstanding performance even in the absence of video-language pre-training, and the results are comparable with or superior to those achieved by state-of-the-art pre-training methods.
Vision-and-language multi-modal pretraining and fine-tuning have shown great success in visual question answering (VQA). Compared to general domain VQA, the performance of biomedical VQA suffers from limited data. In this paper, we propose a retrieval-augmented pretrain-and-finetune paradigm named RAMM for biomedical VQA to overcome the data limitation issue. Specifically, we collect a new biomedical dataset named PMCPM which offers patient-based image-text pairs containing diverse patient situations from PubMed. Then, we pretrain the biomedical multi-modal model to learn visual and textual representation for image-text pairs and align these representations with image-text contrastive objective (ITC). Finally, we propose a retrieval-augmented method to better use the limited data. We propose to retrieve similar image-text pairs based on ITC from pretraining datasets and introduce a novel retrieval-attention module to fuse the representation of the image and the question with the retrieved images and texts. Experiments demonstrate that our retrieval-augmented pretrain-and-finetune paradigm obtains state-of-the-art performance on Med-VQA2019, Med-VQA2021, VQARAD, and SLAKE datasets. Further analysis shows that the proposed RAMM and PMCPM can enhance biomedical VQA performance compared with previous resources and methods. The pre-trained models and codes are published at https://github.com/GanjinZero/RAMM.
Dynamic Contrastive Learning with Pseudo-samples Intervention for Weakly Supervised Joint Video MR and HD
PDF ↗Joint video moment retrieval (MR) and highlight detection (HD) aims to find relevant video moments according to the query text. Existing methods are fully supervised based on manual annotation, and their coarse multi-modal information interactions easily lose details about video and text. In addition, some tasks introduce weakly supervised learning with random masks, while the single masking forces the model to focus on masked words and ignore multi-modal contextual information. In view of this, we attempt weakly supervised joint tasks (MR+HD) and propose Dynamic Contrastive Learning with Pseudo-Sample Intervention (CPI) for better multi-modal video comprehension. First, we design pseudo-samples over random masks for a more efficient contrastive learning manner. We introduce a proportional sampling strategy for pseudo-samples to ensure the semantic difference between the pseudo-samples and the query text. This balances the over-reliance from single random mask to global text semantics and makes the model learn multimodal context from each word fairly. Second, we design dynamic intervention contrastive loss to enhance the core feature-matching ability of the model dynamically. We add pseudo-sample intervention when negative proposals are close to positive proposals. This can help the model overcome the vision confusion phenomenon and achieve semantic similarity instead of word similarity. Extensive experiments demonstrate the effectiveness of CPI and the potential of weakly supervised joint tasks.
Cross-modal recipe retrieval is an emerging visual-textual retrieval task, which aims at matching food images with the corresponding recipes. Although large-scale Vision-Language Pre-training (VLP) models have achieved impressive performance on a wide range of downstream tasks, they still perform unsatisfactorily on this cross-modal retrieval task due to the following two problems: (1) Features from food images and recipes need to be aligned, simply fine-tuning the pre-trained VLP model's image encoder does not explicitly help with this goal. (2) The text content in the recipe is more structured than the text caption in the VLP model's pre-training corpus, which prevents the VLP model from adapting to the recipe retrieval task. In this paper, we propose a Component-aware Instance-specific Prompt learning (CIP) model that fully exploits the ability of large-scale VLP models. CIP enables us to learn the structured recipe information and therefore allows for aligning visual-textual representations without fine-tuning. Furthermore, we construct a recipe encoder termed Adaptive Recipe Merger (ARM) based on hierarchical Transformers, encouraging the model to learn more effective recipe representations. Extensive experiments on the public Recipe1M dataset demonstrate the superiority of our proposed method by outperforming the state-of-the-art methods on cross-modal recipe retrieval task.
The tremendous progress of vision-to-language retrieval over these years is fueled by contrastive vision-language pretraining (VLP), such as CLIP. Although, contrastive methods do not exhibit the same level of performance on other downstream tasks (e.g., video question answering and natural language grounding). One possible reason is they ignore the misalignment between vision and language, especially the absence of spatial information in language. To mitigate this issue, We start from a new perspective and propose a contrastive VLP framework with spatial reconstruction on text (SpaceCLIP). Specifically, we introduce a unique reconstruction method to assign text representations into the same spatial structure with images or videos and a pretraining objective, SpatialNCE, to reduce the computational overhead and ensure performance on downstream tasks. Empirically, we show SpaceCLIP outperforms other methods with performance gains ranging from 2.1% up to 9.0% on MSRVTT and EgoCLIP multiple-choice questions answering, 2.5% up to 11.0% on EPIC-KITCHENS-100 and MSRVTT multi-instance retrieval, and 0.31% up to 7.2% on Ego4D natural language query benchmark.
In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP. Different from previous CLIP-based methods mainly considering feature generalization on visual encoding, we propose a symmetrical distillation strategy (SDS) that further captures the linguistic knowledge in the CLIP text encoder. By cascading the CLIP image encoder with the reversed CLIP text encoder, a symmetrical structure is built with an image-to-text feature flow that covers not only visual but also linguistic information for distillation. Benefiting from the natural alignment in CLIP, such guidance flow provides a progressive optimization objective from vision to language, which can supervise the STR feature forwarding process layer-by-layer. Besides, a new Linguistic Consistency Loss (LCL) is proposed to enhance the linguistic capability by considering second-order statistics during the optimization. Overall, CLIP-OCR is the first to design a smooth transition between image and text for the STR task. Extensive experiments demonstrate the effectiveness of CLIP-OCR with 93.8% average accuracy on six popular STR benchmarks. Code will be available at https://github.com/wzx99/CLIPOCR.
With the emergence of VR and AR, 360° data attracts increasing attention from the computer vision and multimedia communities. Typically, 360° data is projected into 2D ERP (equirectangular projection) images for feature extraction. However, existing methods cannot handle the distortions that result from the projection, hindering the development of 360-data-based tasks. Therefore, in this paper, we propose a Transformer-based model called DATFormer to address the distortion problem. We tackle this issue from two perspectives. Firstly, we introduce two distortion-adaptive modules. The first is a Distortion Mapping Module, which guides the model to pre-adapt to distorted features globally. The second module is a Distortion-Adaptive Attention Block that reduces local distortions on multi-scale features. Secondly, to exploit the unique characteristics of 360° data, we present a learnable relation matrix and use it as part of the positional embedding to further improve performance. Extensive experiments are conducted on three public datasets, and the results show that our model outperforms existing 2D SOD (salient object detection) and 360 SOD methods. The source code is available at https://github.com/yjzhao19981027/DATFormer/.