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Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

Backdoor Attacks Against Deep Image Compression via Adaptive...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yu, Yi Wang, Yufei Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Nanyang Technol Univ IGP ROSE Singapore Singapore

ISBN: (纸本)9798350301298

Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable to backdoor attacks, where some specific trigger patterns added to the input can lead to malicious behavior of the models. In this paper, we present a novel backdoor attack with multiple triggers against learned image compression models. Motivated by the widely used discrete cosine transform (DCT) in existing compression systems and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives for various attacking scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality;2) attacking task-driven measures, such as down-stream face recognition and semantic segmentation. Moreover, a novel simple dynamic loss is designed to balance the influence of different loss terms adaptively, which helps achieve more efficient training. Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.

关键词： Low-level vision

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Leveraging per Image-Token Consistency for vision-Language Pre-training

Leveraging per Image-Token Consistency for Vision-Language P...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gou, Yunhao Ko, Tom Yang, Hansi Kwok, James Zhang, Yu Wang, Mingxuan Southern Univ Sci & Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China ByteDance Ai Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://***/gyhdog99/epic

关键词： Multi-modal learning

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ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector

ScaleKD: Distilling Scale-Aware Knowledge in Small Object De...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Yichen Zhou, Qiqi Liu, Ning Xu, Zhiyuan Ou, Zhicai Mou, Xiaofeng Tang, Jian Midea Grp Foshan Peoples R China Shanghai Univ Elect Power Shanghai Peoples R China

ISBN: (纸本)9798350301298

Despite the prominent success of general object detection, the performance and efficiency of Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distillation (ScaleKD), which transfers knowledge of a complex teacher model to a compact student model. We design two novel modules to boost the quality of knowledge transfer in distillation for SOD: 1) a scale-decoupled feature distillation module that disentangled teacher's feature representation into multi-scale embedding that enables explicit feature mimicking of the student model on small objects. 2) a cross-scale assistant to refine the noisy and uninformative bounding boxes prediction student models, which can mislead the student model and impair the efficacy of knowledge distillation. A multi-scale cross-attention layer is established to capture the multi-scale semantic information to improve the student model. We conduct experiments on COCO and VisDrone datasets with diverse types of models, i.e., two-stage and one-stage detectors, to evaluate our proposed method. Our ScaleKD achieves superior performance on general detection performance and obtains spectacular improvement regarding the SOD performance.

关键词： Efficient and scalable vision

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COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hogeweg, Laurens E. Gangireddy, Rajesh Brunink, Django Kalkman, Vincent J. Cornelissen, Ludo Kamminga, Jacob W. Intel Eindhoven Netherlands Nat Biodivers Ctr Leiden Netherlands Univ Twente Enschede Netherlands

ISBN: (纸本)9798350365474

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： anomaly image recognition novel class

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OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ryu, Jeongun Puche, Aaron Valero Shin, JaeWoong Park, Seonwook Brattoli, Biagio Lee, Jinhee Jung, Wonkyung Cho, Soo Ick Paeng, Kyunghyun Ock, Chan-Young Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://***/research/publications/ocelot are a crucial starting point toward the important research direction of incorporating celltissue relationships in computation pathology.

关键词： cell microscopy Medical and biological vision

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Learning a Simple Low-light Image Enhancer from Paired Low-light Instances

Learning a Simple Low-light Image Enhancer from Paired Low-l...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fu, Zhenqi Yang, Yan Tu, Xiaotong Huang, Yue Ding, Xinghao Ma, Kai-Kuang Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9798350301298

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https: //***/zhenqifu/PairLIE.

关键词： Low-level vision

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FAME-ViL: Multi-Tasking vision-Language Model for Heterogeneous Fashion Tasks

FAME-ViL: Multi-Tasking Vision-Language Model for Heterogene...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Han, Xiao Zhu, Xiatian Yu, Licheng Zhang, Li Song, Yi-Zhe Xiang, Tao Univ Surrey CVSSP Guildford Surrey England IFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Surrey Inst People Ctr Artificial Intelligenc Guildford Surrey England Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+L model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://***/BrandonHanx/FAME-ViL.

关键词： and reasoning language vision

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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Visibility Constrained Wide-band Illumination Spectrum Desig...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Niu, Muyao Li, Zhuoxiao Zhong, Zhihang Zheng, Yinqiang Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350301298

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://***/MyNiuuu/VCSD.

关键词： Physics-based vision and shape-from-X

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Robust 3D Shape Classification via Non-local Graph Attention Network

Robust 3D Shape Classification via Non-local Graph Attention...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qin, Shengwei Li, Zhong Liu, Ligang Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou Peoples R China Huzhou Univ Sch Informat Huzhou Peoples R China Zhejiang Sci Tech Univ Sch Sci Hangzhou Zhejiang Peoples R China Univ Sci & Technol China Sch Math Sci Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We introduce a non-local graph attention network (NL-GAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.

关键词： detection recognition: Categorization retrieval

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Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient vision Transformers

Beyond Attentive Tokens: Incorporating Token Importance and ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Long, Sifan Zhao, Zhen Pi, Jimin Wang, Shengsheng Wang, Jingdong Jilin Univ Coll Comp Sci & Technol Jilin Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Jilin Jilin Peoples R China Baidu VIS Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we de-couple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%.

关键词： Deep learning architectures and techniques

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