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DeAR: Debiasing vision-Language Models with Additive Residuals

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

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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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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

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

作者： Gillert, Alexander Resente, Giulia Anadon-Rosell, Alba Wilmking, Martin von Lukas, Uwe Freiherr Fraunhofer Inst Comp Graph Res IGD Rostock Germany Ernst Moritz Arndt Univ Inst Bot & Landscape Ecol Greifswald Germany Ctr Res Ecol & Forestry Applicat CREAF Barcelona Spain Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798350301298

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

关键词： cell microscopy Medical and biological vision

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Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Guiding Pseudo-labels with Uncertainty Estimation for Source...

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

作者： Litrico, Mattia Del Bue, Alessio Morerio, Pietro Ist Italiano Tecnol Pattern Anal & Comp Vision PAVIS Genoa Italy

ISBN: (纸本)9798350301298

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

关键词： continual low-shot meta or long-tail learning Transfer

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DETRs with Hybrid Matching

DETRs with Hybrid Matching

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

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

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PATS: Patch Area Transportation with Subdivision for Local Feature Matching

PATS: Patch Area Transportation with Subdivision for Local F...

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

作者： Ni, Junjie Li, Yijin Huang, Zhaoyang Li, Hongsheng Bao, Hujun Cui, Zhaopeng Zhang, Guofeng Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China ZJU SenseTime Joint Lab 3D Vision Hangzhou Peoples R China Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at https://***/pats/.

关键词： 3D from multi-view and sensors

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DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD: Line Segment Detection and Refinement with Deep Ima...

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

作者： Pautrat, Remi Barath, Daniel Larsson, Viktor Oswald, Martin R. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Lund Univ Lund Sweden Univ Amsterdam Amsterdam Netherlands Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9798350301298

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://***/cvg/DeepLSD.

关键词： Low-level vision

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Recurrent vision Transformers for Object Detection with Event Cameras

Recurrent Vision Transformers for Object Detection with Even...

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

作者： Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

We present Recurrent vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: first, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (< 12 ms on a T4 GPU) and favorable parameter efficiency (5x fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. Code: https://***/uzh-rpg/RVT

关键词： detection recognition: Categorization retrieval

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R²Former: Unified Retrieval and Reranking Transformer for Place recognition

R<SUP>2</SUP>Former: Unified Retrieval and Reranking Transfo...

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

作者： Zhu, Sijie Yang, Linjie Chen, Chen Shah, Mubarak Shen, Xiaohui Wang, Heng ByteDance Beijing Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9798350301298

Visual Place recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named R(2)Former. The proposed reranking module takes feature correlation, attention value, and xy coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, R(2)Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://***/Jeff-Zilence/R2Former.

关键词： detection recognition: Categorization retrieval

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METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

METransformer: Radiology Report Generation by Transformer wi...

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

作者： Wang, Zhanyu Liu, Lingqiao Wang, Lei Zhou, Luping Univ Sydney Sydney NSW Australia Univ Adelaide Adelaide SA Australia Univ Wollongong Wollongong NSW Australia

ISBN: (纸本)9798350301298

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance.

关键词： and reasoning language vision

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Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

Shortcomings of Top-Down Randomization-Based Sanity Checks f...

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

作者： Binder, Alexander Weber, Leander Lapuschkin, Sebastian Montavon, Gregoire Muller, Klaus-Robert Samek, Wojciech SIT Singapore IICT Cluster Singapore Singapore UiO Oslo Oslo Norway Fraunhofer HHI Berlin Germany FU Berlin Berlin Germany BIFOLD Berlin Berlin Germany TU Berlin Berlin Germany Korea Univ Seoul Seoul South Korea MPI Saarbrucken Saarbrucken Germany SUTD Singapore Singapore Singapore

ISBN: (纸本)9798350301298

While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing can be overinterpreted if regarded as a primary criterion for selecting or discarding explanation methods. To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e.g. [20]). We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Firstly, we show that uninformative attribution maps created with zero pixel-wise covariance easily achieve high scores in this type of checks. Secondly, we show that top-down model randomization preserves scales of forward pass activations with high probability. That is, channels with large activations have a high probility to contribute strongly to the output, even after randomization of the network on top of them. Hence, explanations after randomization can only be expected to differ to a certain extent. This explains the observed experimental gap. In summary, these results demonstrate the inadequacy of model-randomization-based sanity checks as a criterion to rank attribution methods.

关键词： Explainable computer vision

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