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DeAR: Debiasing vision-Language Models with Additive Residuals

DeAR: Debiasing Vision-Language Models with Additive Residua...

引用

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

作者： Seth, Ashish Hemani, Mayur Agarwal, Chirag IIT Madras Madras India Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable image and text representations. However, these models suffer from societal biases owing to the skewed distribution of various identity groups in the training data. These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications. In this work, we present DEAR (Debiasing with Additive Residuals), a novel debiasing method that learns additive residual image representations to offset the original representations, ensuring fair output representations. In doing so, it reduces the ability of the representations to distinguish between the different identity groups. Further, we observe that the current fairness tests are performed on limited face image datasets that fail to indicate why a specific text concept should/should not apply to them. To bridge this gap and better evaluate DEAR, we introduce the PROTECTED ATTRIBUTE TAG ASSOCIATION (PATA) dataset - a new context-based bias benchmarking dataset for evaluating the fairness of large pre-trained VLMs. Additionally, PATA provides visual context for a diverse human population in different scenarios with both positive and negative connotations. Experimental results for fairness and zero-shot performance preservation using multiple datasets demonstrate the efficacy of our framework. The dataset is released here.

关键词： accountability ethics in vision fairness privacy Transparency

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CIPPSRNet: A Camera Internal Parameters Perception Network Based Contrastive Learning for Thermal Image Super-Resolution

CIPPSRNet: A Camera Internal Parameters Perception Network B...

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

作者： Wang, Kai Sun, Qigong Wang, Yicheng Wei, Huiyuan Lv, Chonghua Tian, Xiaolin Liu, Xu SenseTime Res Hong Kong Peoples R China Xidian Univ Xian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Thermal Image Super-Resolution (TISR) is a technique for converting Low-Resolution (LR) thermal images to High-Resolution (HR) thermal images. This technique has recently become a research hotspot due to its ability to reduce sensor costs and improve visual perception. However, current research does not provide an effective solution for multi-sensor data training, possibly driven by pixel mismatch and simple degradation setting issues. In this paper, we proposed a Camera Internal Parameters Perception Network (CIPPSRNet) for LR thermal image enhancement. The camera internal parameters (CIP) were explicitly modeled as a feature representation, the LR features were transformed into the intermediate domain containing the internal parameters information by perceiving CIP representation. The mapping between the intermediate domain and the spatial domain of the HR features was learned by CIPPSRNet. In addition, we introduced contrastive learning to optimize the pretrained Camera Internal Parameters Representation Network and the feature encoders. Our proposed network is capable of achieving a more efficient transformation from the LR to the HR domains. Additionally, the use of contrastive learning can improve the network's adaptability to misalignment data with insufficient pixel matching and its robustness. Experiments on PBVS2022 TISR Dataset show that our network has achieved state-of-the-art performance for the Thermal SR task.

关键词： Training Degradation Visualization Superresolution Cameras Robustness pattern recognition

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Simple and Efficient Architectures for Semantic Segmentation

Simple and Efficient Architectures for Semantic Segmentation

引用

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

作者： Mehta, Dushyant Skliar, Andrii Ben Yahia, Haitam Borse, Shubhankar Porikli, Fatih Habibian, Amirhossein Blankevoort, Tijmen Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware. This paper demonstrates that a simple encoder-decoder architecture with a ResNet-like backbone and a small multi-scale head, performs on-par or better than complex semantic segmentation architectures such as HRNet, FANet and DDRNets. Naively applying deep backbones designed for Image Classification to the task of Semantic Segmentation leads to sub-par results, owing to a much smaller effective receptive field of these backbones. Implicit among the various design choices put forth in works like HRNet, DDRNet, and FANet are networks with a large effective receptive field. It is natural to ask if a simple encoder-decoder architecture would compare favorably if comprised of backbones that have a larger effective receptive field, though without the use of inefficient operations like dilated convolutions. We show that with minor and inexpensive modifications to ResNets, enlarging the receptive field, very simple and competitive baselines can be created for Semantic Segmentation. We present a family of such simple architectures for desktop as well as mobile targets, which match or exceed the performance of complex models on the Cityscapes dataset. We hope that our work provides simple yet effective baselines for practitioners to develop efficient semantic segmentation models. The model definitions and pre-trained weights are available at https://***/Qualcomm-AI-research/FFNet.

关键词： Image segmentation Head Semantics Graphics processing units computer architecture Hardware Distance measurement

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Language-Guided Audio-Visual Source Separation via Trimodal Consistency

Language-Guided Audio-Visual Source Separation via Trimodal ...

引用

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

作者： Tan, Reuben Ray, Arijit Burns, Andrea Plummer, Bryan A. Salamon, Justin Nieto, Oriol Russell, Bryan Saenko, Kate Boston Univ Boston MA 02215 USA Adobe Res San Francisco CA USA IBM Res MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training. Our project page including publicly available code can be found at https://***/rxtan/projects/VAST.

关键词： and reasoning language vision

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Contrastive Grouping with Transformer for Referring Image Segmentation

Contrastive Grouping with Transformer for Referring Image Se...

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

作者： Tang, Jiajin Zheng, Ge Shi, Cheng Yang, Sibei ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350301298

Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression. Existing one-stage methods employ per-pixel classification frameworks, which attempt straightforwardly to align vision and language at the pixel level, thus failing to capture critical object-level information. In this paper, we propose a mask classification framework, Contrastive Grouping with Transformer network (CGFormer), which explicitly captures object-level information via token-based querying and grouping strategy. Specifically, CGFormer first introduces learnable query tokens to represent objects and then alternately queries linguistic features and groups visual features into the query tokens for object-aware cross-modal reasoning. In addition, CGFormer achieves crosslevel interaction by jointly updating the query tokens and decoding masks in every two consecutive layers. Finally, CGFormer cooperates contrastive learning to the grouping strategy to identify the token and its mask corresponding to the referent. Experimental results demonstrate that CGFormer outperforms state-of-the-art methods in both segmentation and generalization settings consistently and significantly. Code is available at https://***/Toneyaya/CGFormer.

关键词： language reasoning vision

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TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in vision-Centric Autonomous Driving

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Jo...

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

作者： Fang, Shaoheng Wang, Zi Zhong, Yiqi Ge, Junhao Chen, Siheng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://***/MediaBrain-SJTU/TBP-Former

关键词： Autonomous driving

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Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography

Physics-Guided ISO-Dependent Sensor Noise Modeling for Extre...

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

作者： Cao, Yue Liu, Ming Liu, Shuai Wang, Xiaotao Lei, Lei Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling. To tap the potential of learning-based sensor noise modeling, we investigate the noise formation in a typical imaging process and propose a novel physics-guided ISO-dependent sensor noise modeling approach. Specifically, we build a normalizing flow-based framework to represent the complex noise characteristics of CMOS camera sensors. Each component of the noise model is dedicated to a particular kind of noise under the guidance of physical models. Moreover, we take into consideration of the ISO dependence in the noise model, which is not completely considered by the existing learning-based methods. For training the proposed noise model, a new dataset is further collected with paired noisy-clean images, as well as flat-field and bias frames covering a wide range of ISO settings. Compared to existing methods, the proposed noise model is equipped with a flexible structure and accurate modeling capabilities, which is beneficial for better denoising performance in extreme low-light scenes. The dataset and code are available at https://***/happycaoyue/LLD.

关键词： Low-level vision

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MAP: Multimodal Uncertainty-Aware vision-Language Pre-training Model

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-traini...

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

作者： Ji, Yatai Wang, Junjie Gong, Yuan Zhang, Lin Zhu, Yanru Wang, Hongfa Zhang, Jiaxing Sakai, Tetsuya Yang, Yujiu Tsinghua Univ Beijing Peoples R China Waseda Univ Tokyo Japan IDEA Gandhinagar India Tencent TEG Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pretraining frameworks and propose suitable pre-training tasks: Distribution-based vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

关键词： Multi-modal learning

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Deep Normalized Cross-Modal Hashing with Bi-Direction Relation Reasoning

Deep Normalized Cross-Modal Hashing with Bi-Direction Relati...

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

作者： Sun, Changchang Latapie, Hugo Liu, Gaowen Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Cisco Res Emerging Technol & Incubat San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.

关键词： computer vision Codes conferences Computational modeling Semantics Bidirectional control Benchmark testing

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

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

作者： Wang, Fan Kong, Adams Wai-Kin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Rapid Rich Object Search ROSE Lab IGP Singapore Singapore

ISBN: (纸本)9798350301298

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks;however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both l(2) and l(infinity)-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

关键词： Explainable computer vision

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