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Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration

Towards Building Self-Aware Object Detectors via Reliable Un...

引用

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

作者： Oksuz, Kemal Joy, Tom Dokania, Puneet K. Five AI Ltd Cambridge England

ISBN: (纸本)9798350301298

The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift;obtain reliable uncertainty estimates for the entire scene;and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at: https://***/fiveai/saod.

关键词： detection recognition: Categorization retrieval

来源：评论

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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

引用

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

作者： Ahousamra, Shahira Gupta, Rajarsi Kurc, Tahsin Samaras, Dimitris Saltz, Joel Chen, Chao SUNY Stony Brook Dept Comp Sci Stony Brook NY 11790 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY USA

ISBN: (纸本)9798350301298

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

关键词： cell microscopy Medical and biological vision

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PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

PolyFormer: Referring Image Segmentation as Sequential Polyg...

引用

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

作者： Liu, Jiang Ding, Hui Cai, Zhaowei Zhang, Yuting Satzoda, Ravi Kumar Mahadevan, Vijay Manmatha, R. Johns Hopkins Univ Baltimore MD 21218 USA AWS AI Labs Pasadena CA USA

ISBN: (纸本)9798350301298

In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transformer (PolyFormer), which takes a sequence of image patches and text query tokens as input, and outputs a sequence of polygon vertices autoregressively. For more accurate geometric localization, we propose a regression-based decoder, which predicts the precise floating-point coordinates directly, without any coordinate quantization error. In the experiments, PolyFormer outperforms the prior art by a clear margin, e.g., 5.40% and 4.52% absolute improvements on the challenging RefCOCO+ and RefCOCOg datasets. It also shows strong generalization ability when evaluated on the referring video segmentation task without fine-tuning, e.g., achieving competitive 61.5% J&F on the Ref-DAVIS17 dataset.

关键词： and reasoning language vision

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

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

作者： Deb, Tonmoay Wang, Lichen Bessinger, Zachary Khosravan, Naji Penner, Eric Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9798350365474

This paper focuses on bridging the gap between natural language descriptions, 360 degrees panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell1. We first introduce an effective technique for extracting geometric information from ZInD's raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naive methods in both tasks, hence, can be considered as a complement to the naive to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： computer vision

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Our Deep CNN Face Matchers Have Developed Achromatopsia

Our Deep CNN Face Matchers Have Developed Achromatopsia

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

作者： Bhatta, Aman Mery, Domingo Wu, Haiyu Annan, Joyce King, Michael C. Bowyer, Kevin W. Univ Notre Dame Notre Dame IN 46556 USA Pontificia Univ Catolica Chile Santiago Chile Florida Insitute Technol Melbourne FL USA FaceTec Las Vegas NV USA

ISBN: (纸本)9798350365474

Modern deep CNN face matchers are trained on datasets containing "color" images. We show that such matchers achieve essentially the same accuracy on color images when trained using only grayscale images. We then consider possible causes for deep CNN face matchers "not using color". Popular web-scraped face datasets actually have 30 to 60% of their identities with one or more grayscale images. We analyze whether this grayscale element in the training set impacts the accuracy achieved, and conclude that it does not. Comparable accuracy for color test images using only grayscale images implies that the inclusion of "color" may not necessarily add any significant information to the recognition of individuals. This also implies the use of computing resources can be optimized to make the training process more efficient using only grayscale images. Utilizing grayscale images for training reduces the memory footprint of the training data, thereby decreasing system processing time during training. Additionally, our findings emphasize that the adoption of grayscale images not only makes face recognition training more efficient but also offers the opportunity to include more training data, which could result in more accurate face recognition models.

关键词： Face recognition

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Region-Aware Pretraining for Open-Vocabulary Object Detection with vision Transformers

Region-Aware Pretraining for Open-Vocabulary Object Detectio...

引用

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

作者： Kim, Dahun Angelova, Anelia Kuo, Weicheng Google Res Brain Team Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We present Region-aware Open-vocabulary vision Transformers (RO-ViT) - a contrastive image-text pretraining recipe to bridge the gap between image-level pretraining and open-vocabulary object detection. At the pretraining phase, we propose to randomly crop and resize regions of positional embeddings instead of using the whole image positional embeddings. This better matches the use of positional embeddings at region-level in the detection finetuning phase. In addition, we replace the common softmax cross entropy loss in contrastive learning with focal loss to better learn the informative yet difficult examples. Finally, we leverage recent advances in novel object proposals to improve open-vocabulary detection finetuning. We evaluate our full model on the LVIS and COCO open-vocabulary detection benchmarks and zero-shot transfer. RO-ViT achieves a state-of-the-art 32.1 AP(r) on LVIS, surpassing the best existing approach by +5.8 points in addition to competitive zero-shot transfer detection. Surprisingly, RO-ViT improves the image-level representation as well and achieves the state of the art on 9 out of 12 metrics on COCO and Flickr image-text retrieval benchmarks, outperforming competitive approaches with larger models.

关键词： language reasoning vision

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Light Source Separation and Intrinsic Image Decomposition under AC Illumination

Light Source Separation and Intrinsic Image Decomposition un...

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

作者： Yoshida, Yusaku Kawahara, Ryo Okabe, Takahiro Kyushu Inst Technol Dept Artificial Intelligence 680-4 Kawazu Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9798350301298

Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the diffuse reflection model, and then show why and how those ambiguities can be resolved via a physics-based approach. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.

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

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Two-way Multi-Label Loss

Two-way Multi-Label Loss

引用

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

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan Univ Tsukuba Tsukuba Japan

ISBN: (纸本)9798350301298

A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by applying a softmax cross-entropy loss, the multi-label task is tackled mainly in a binary cross-entropy (BCE) framework. In contrast to the softmax loss, the BCE loss involves issues regarding imbalance as multiple classes are decomposed into a bunch of binary classifications;recent works improve the BCE loss to cope with the issue by means of weighting. In this paper, we propose a multi-label loss by bridging a gap between the softmax loss and the multi-label scenario. The proposed loss function is formulated on the basis of relative comparison among classes which also enables us to further improve discriminative power of features by enhancing classification margin. The loss function is so flexible as to be applicable to a multi-label setting in two ways for discriminating classes as well as samples. In the experiments on multi-label classification, the proposed method exhibits competitive performance to the other multi-label losses, and it also provides transferrable features on single-label ImageNet training. Codes are available at https: //***/tk1980/TwowayMultiLabelLoss.

关键词： detection recognition: Categorization retrieval

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GIVL: Improving Geographical Inclusivity of vision-Language Models with Pre-Training Methods

GIVL: Improving Geographical Inclusivity of Vision-Language ...

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

作者： Yin, Da Gao, Feng Thattai, Govind Johnston, Michael Chang, Kai -Wei Univ Calif Los Angeles Los Angeles CA 90095 USA Amazon Alexa AI Lexington MA USA

ISBN: (纸本)9798350301298

A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural differences. If a model is unaware of regional characteristics, it may lead to performance disparity across regions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geodiverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we design new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V&L tasks.

关键词： language reasoning vision

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Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception

Collecting Cross-Modal Presence-Absence Evidence for Weakly-...

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

作者： Gao, Junyu Chen, Mengyuan Xu, Changsheng Chinese Acad Sci CASIA Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

With only video-level event labels, this paper targets at the task of weakly-supervised audio-visual event perception (WS-AVEP), which aims to temporally localize and categorize events belonging to each modality. Despite the recent progress, most existing approaches either ignore the unsynchronized property of audio-visual tracks or discount the complementary modality for explicit enhancement. We argue that, for an event residing in one modality, the modality itself should provide ample presence evidence of this event, while the other complementary modality is encouraged to afford the absence evidence as a reference signal. To this end, we propose to collect Cross-Modal Presence-Absence Evidence (CMPAE) in a unified framework. Specifically, by leveraging uni-modal and cross-modal representations, a presence-absence evidence collector (PAEC) is designed under Subjective Logic theory. To learn the evidence in a reliable range, we propose a joint-modal mutual learning (JML) process, which calibrates the evidence of diverse audible, visible, and audi-visible events adaptively and dynamically. Extensive experiments show that our method surpasses state-of-the-arts (e.g., absolute gains of 3.6% and 6.1% in terms of event-level visual and audio metrics). Code is available in ***/MengyuanChen21/cvpr2023-CMPAE.

关键词： 2023 ieee/CVF conference on computer vision and pattern recognition (cvpr)

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