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Validating Privacy-Preserving Face recognition under a Minimum Assumption

Validating Privacy-Preserving Face Recognition under a Minim...

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

作者： Zhang, Hui Dong, Xingbo Lai, YenLung Zhou, Ying Zhang, Xiaoyan Lv, Xingguo Jin, Zhe Li, Xuejun Anhui Univ Anhui Prov Key Lab Secure Artificial Intelligence Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

The widespread use of cloud-based face recognition technology raises privacy concerns, as unauthorized access to face images can expose personal information or be exploited for fraudulent purposes. In response, privacy-preserving face recognition (PPFR) schemes have emerged to hide visual information and thwart unauthorized access. However, the validation methods employed by these schemes often rely on unrealistic assumptions, leaving doubts about their true effectiveness in safeguarding facial privacy. In this paper, we introduce a new approach to privacy validation called Minimum Assumption Privacy Protection Validation (Map(2)V). This is the first exploration of formulating a privacy validation method utilizing deep image priors and zeroth-order gradient estimation, with the potential to serve as a general framework for PPFR evaluation. Building upon Map(2)V, we comprehensively validate the privacy-preserving capability of PPFRs through a combination of human and machine vision. The experiment results and analysis demonstrate the effectiveness and generalizability of the proposed Map(2)V, showcasing its superiority over native privacy validation methods from PPFR works of literature. Additionally, this work exposes privacy vulnerabilities in evaluated state-of-the-art PPFR schemes, laying the foundation for the subsequent effective proposal of countermeasures. The source code is available at https://***/Beauty9882/MAP2V.

关键词： Deep Generative Prior Minimum Assumption Privacy Validation Privacy-Preserving Face recognition Zeroth-order Gradient Estimation

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Towards Better vision-Inspired vision-Language Models

Towards Better Vision-Inspired Vision-Language Models

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

作者： Cao, Yun-Hao Ji, Kaixiang Huang, Ziyuan Zheng, Chuanyang Liu, Jiajia Wang, Jian Chen, Jingdong Yang, Ming Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Ant Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

vision-language (VL) models have achieved unprecedented success recently, in which the connection module is the key to bridge the modality gap. Nevertheless, the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side, most existing approaches only use the last feature of the vision tower, without using the low-level features. On the language side, most existing methods only introduce shallow vision-language interactions. In this paper, we present a vision-inspired vision-language connection module, dubbed as VIVL, which efficiently exploits the vision cue for VL models. To take advantage of the lower-level information from the vision tower, a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers, which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions, we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of- the-art method by 18.1 CIDEr when training from scratch on the COCO caption task, which greatly improves the data efficiency. When used as a plug-in module, VIVL consistently improves the performance for various backbones and VL frameworks, delivering new state-of-the-art results on multiple benchmarks, e.g., NoCaps and VQAv2.

关键词： deep learning vision-language models feature pyramid deep prompt

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

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

作者： Noor, Nadhira Jametoni, Fabianaugie Kim, Jinbeom Hong, Hyunsu Park, In Kyu Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Finedigital Inc Seongnam Si 13496 Gyeonggi Do South Korea

ISBN: (纸本)9798350365474

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Action recognition convolutional neural network embedded system skeleton-based

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LowRankOcc: Tensor Decomposition and Low-Rank Recovery for vision-based 3D Semantic Occupancy Prediction

LowRankOcc: Tensor Decomposition and Low-Rank Recovery for V...

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

作者： Zhao, Linqing Xu, Xiuwei Wang, Ziwei Zhang, Yunpeng Zhang, Borui Zheng, Wenzhao Du, Dalong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids, but the sparsity of non-empty voxels introduces considerable spatial redundancy, leading to potential overfitting risks. In contrast, our approach leverages the intrinsic low-rank property of 3D occupancy data, factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically, we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding recovery" framework, we encode 3D contexts with only 1/2D convolutions and poolings, and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.

关键词： 3D semantic occupancy tensor decomposition

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3D Human Pose Perception from Egocentric Stereo Videos

3D Human Pose Perception from Egocentric Stereo Videos

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

作者： Akada, Hiroyasu Wang, Jian Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2, UnrealEgo-RW, and trained models are available on our project page(1) and Benchmark Challenge(2).

关键词： Egocentric 3D human pose estimation First-person view Stereo vision

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Language-only Efficient Training of Zero-shot Composed Image Retrieval

Language-only Efficient Training of Zero-shot Composed Image...

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

作者： Gu, Geonmo Chun, Sanghyuk Kim, Wonjae Kang, Yoohoon Yun, Sangdoo NAVER Vis Seongnam South Korea NAVER AI Lab Bundangdong South Korea

ISBN: (纸本)9798350353006

Composed image retrieval (CIR) task takes a composed query of image and text, aiming to search relative images for both conditions. Conventional CIR approaches need a training dataset composed of triplets of query image, query text, and target image, which is very expensive to collect. Several recent works have worked on the zero-shot (ZS) CIR paradigm to tackle the issue without using pre-collected triplets. However, the existing ZS-CIR methods show limited backbone scalability and generalizability due to the lack of diversity of the input texts during training. We propose a novel CIR framework, only using language for its training. Our LinCIR (Language-only training for CIR) can be trained only with text datasets by a novel self-supervision named self-masking projection (SMP). We project the text latent embedding to the token embedding space and construct a new text by replacing the keyword tokens of the original text. Then, we let the new and original texts have the same latent embedding vector. With this simple strategy, LinCIR is surprisingly efficient and highly effective;LinCIR with CLIP ViT-G backbone is trained in 48 minutes and shows the best ZS-CIR performances on four different CIR benchmarks, CIRCO, GeneCIS, FashionIQ, and CIRR, even outperforming supervised method on FashionIQ. Code is available at ***/navervision/lincir

关键词： composed image retrieval language-only training vision and language zero-shot composed image retrieval zs-cir

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Revisiting Counterfactual Problems in Referring Expression Comprehension

Revisiting Counterfactual Problems in Referring Expression C...

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

作者： Yu, Zhihan Li, Ruifan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However, these methods focus on the overall image-text or specific attribute mismatch only. In this paper, we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim, we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically, we leverage pre-trained language model such as BERT to modify the attribute words in the queries, obtaining the corresponding counterfactual samples. Furthermore, we propose a C-REC framework. We first adopt three encoders to extract image, text and attribute features. Then, our dual-branch attentive fusion module fuses these crossmodal features with two branches by an attention mechanism. At last, two prediction heads generate a bounding box and a counterfactual label, respectively. In addition, we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://***/Glacier0012/CREC.

关键词： Counterfactual Generation Referring Expression Comprehension vision and Language

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Boosting Continual Learning of vision-Language Models via Mixture-of-Experts Adapters

Boosting Continual Learning of Vision-Language Models via Mi...

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

作者： Yu, Jiazuo Zhuge, Yunzhi Zhang, Lu Hu, Ping Wang, Dong Lu, Huchuan He, You Dalian Univ Technol Dalian Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353006

Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout life-long learning and (ii) significant computational burdens associated with full-model tuning. In this work, we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models, we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively. Through extensive experiments across various settings, our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%. Our code locates at https://***/JiazuoYu/MoE-Adapters4CL

关键词： class incremental learning continual learning task incremental learning vision-Language Model

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MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-T...

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

作者： Agiza, Ahmed Neseem, Marina Reda, Sherief Brown Univ Providence RI 02912 USA

ISBN: (纸本)9798350353006

Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MTLoRA, a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively disentangle the parameter space in MTL fine-tuning, thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks, outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency. Our code is publicly available.(1)

关键词： efficient multi-task learning hierarchical transformers LoRA low-rank adapatation multi-task learning parameter-efficient training PEFT vision transformers

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Sharingan: A Transformer Architecture for Multi-Person Gaze Following

Sharingan: A Transformer Architecture for Multi-Person Gaze ...

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

作者： Tafasca, Samy Gupta, Anshul Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350353013;9798350353006

Gaze is a powerful form of non-verbal communication that humans develop from an early age. As such, modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular, the gaze following task in computer vision is defined as the prediction of the 2D pixel coordinates where a person in the image is looking. Previous attempts in this area have primarily centered on CNN-based architectures, but they have been constrained by the need to process one person at a time, which proves to be highly inefficient. In this paper, we introduce a novel and effective multi-person transformer-based architecture for gaze prediction. While there exist prior works using transformers for multi-person gaze prediction [38, 39], they use a fixed set of learnable embeddings to decode both the person and its gaze target, which requires a matching step afterward to link the predictions with the annotations. Thus, it is difficult to quantitatively evaluate these methods reliably with the available benchmarks, or integrate them into a larger human behavior understanding system. Instead, we are the first to propose a multi-person transformer-based architecture that maintains the original task formulation and ensures control over the people fed as input. Our main contribution lies in encoding the person-specific information into a single controlled token to be processed alongside image tokens and using its output for prediction based on a novel multiscale decoding mechanism. Our new architecture achieves state-of-the-art results on the GazeFollow, VideoAttentionTarget, and ChildPlay datasets and outper-forms comparable multi-person architectures with a notable margin. Our code, checkpoints, and data extractions will be made publicly available soon.

关键词： computer vision deep learning gaze following

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