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ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification

ISAR: A Benchmark for Single- and Few-Shot Object Instance S...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Gorlo, Nicolas Blomqvist, Kenneth Milano, Francesco Siegwart, Roland Swiss Fed Inst Technol Autonomous Syst Lab Zurich Switzerland

ISBN: (纸本)9798350318920;9798350318937

Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class, we need to build a large dataset and retrain the system. To build spatial AI systems that can quickly be taught about new objects, we need to effectively solve the problem of single-shot object detection, instance segmentation and re-identification. So far there is neither a method fulfilling all of these requirements in unison nor a benchmark that could be used to test such a method. Addressing this, we propose ISAR, a benchmark and baseline method for single- and few-shot object Instance Segmentation And Re-identification, in an effort to accelerate the development of algorithms that can robustly detect, segment, and re-identify objects from a single or a few sparse training examples. We provide a semi-synthetic dataset of video sequences with ground-truth semantic annotations, a standardized evaluation pipeline, and a baseline method. Our benchmark aligns with the emerging research trend of unifying Multi-Object Tracking, Video Object Segmentation, and Re-identification.

关键词： 3D computer vision Algorithms Algorithms Algorithms Datasets and evaluations Video recognition and understanding

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CLRerNet: Improving Confidence of Lane Detection with LaneIoU

CLRerNet: Improving Confidence of Lane Detection with LaneIo...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Honda, Hiroto Uchida, Yusuke GO Inc Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

autonomous driving and driver assistance systems. Modern deep lane detection methods with anchor-based lane representation exhibit excellent performance on lane detection benchmarks. Through preliminary oracle experiments, we firstly disentangle the lane representation components to determine the direction of our approach. We show that correct lane positions are already among the predictions of an existing anchor-based detector, and the confidence scores that accurately represent intersection-over-union (IoU) with ground truths are the most beneficial. Based on the finding, we propose LaneIoU that better correlates with the metric, by taking the local lane angles into consideration. We develop a novel detector coined CLRerNet featuring LaneIoU for the target assignment cost and loss functions aiming at the improved quality of confidence scores. Through careful and fair benchmark including cross validation, we demonstrate that CLRerNet outperforms the state-of-the-art by a large margin - enjoying F1 score of 81.43% compared with 80.47% of the existing method on CULane, and 86.47% compared with 86.10% on CurveLanes. Code and models are available at https: //***/hirotomusiker/CLRerNet.

关键词： Algorithms Applications Autonomous Driving Image recognition and understanding

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Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels

Exploiting CLIP for Zero-shot HOI Detection Requires Knowled...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wan, Bo Tuytelaars, Tinne Katholieke Univ Leuven ESAT Leuven Belgium

ISBN: (纸本)9798350318920;9798350318937

In this paper, we investigate the task of zero-shot human-object interaction (HOI) detection, a novel paradigm for identifying HOIs without the need for task-specific annotations. To address this challenging task, we employ CLIP, a large-scale pre-trained vision-language model (VLM), for knowledge distillation on multiple levels. Specifically, we design a multi-branch neural network that leverages CLIP for learning HOI representations at various levels, including global images, local union regions encompassing human-object pairs, and individual instances of humans or objects. To train our model, CLIP is utilized to generate HOI scores for both global images and local union regions that serve as supervision signals. The extensive experiments demonstrate the effectiveness of our novel multi-level CLIP knowledge integration strategy. Notably, the model achieves strong performance, which is even comparable with some fully-supervised and weakly-supervised methods on the public HICO-DET benchmark. Code is available at https://***/bobwan1995/Zeroshot-HOI-with-CLIP.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

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Efficient Semantic Matching with Hypercolumn Correlation

Efficient Semantic Matching with Hypercolumn Correlation

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kim, Seungwook Min, Juhong Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9798350318920;9798350318937

Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the cost of increased computation and latency. In this work, we focus on the aspect that the performance improvements of recent methods can also largely be attributed to the usage of multi-scale correlation maps, which hold various information ranging from low-level geometric cues to high-level semantic contexts. To this end, we propose HCCNet, an efficient yet effective semantic matching method which exploits the full potential of multi-scale correlation maps, while eschewing the reliance on expensive matchwise relationship mining on the 4D correlation map. Specifically, HCCNet performs feature slicing on the bottleneck features to yield a richer set of intermediate features, which are used to construct a hypercolumn correlation. HCCNet can consequently establish semantic correspondences in an effective manner by reducing the volume of conventional high-dimensional convolution or self-attention operations to efficient point-wise convolutions. HCCNet demonstrates state-of-the-art or competitive performances on the standard benchmarks of semantic matching, while incurring a notably lower latency and computation overhead compared to the existing SoTA methods.

关键词： 3D computer vision Algorithms Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Towards Addressing the Misalignment of Object Proposal Evaluation for vision-Language Tasks via Semantic Grounding

Towards Addressing the Misalignment of Object Proposal Evalu...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Feinglass, Joshua Yang, Yezhou Arizona State Univ Act Percept Grp Tempe AZ 85287 USA

ISBN: (纸本)9798350318920;9798350318937

Object proposal generation serves as a standard pre-processing step in vision-Language (VL) tasks (image captioning, visual question answering, etc.). The performance of object proposals generated for VL tasks is currently evaluated across all available annotations, a protocol that we show is "misaligned" - higher scores do not necessarily correspond to improved performance on downstream VL tasks. Our work serves as a study of this phenomenon and explores the effectiveness of semantic grounding to mitigate its effects. To this end, we propose evaluating object proposals against only a subset of available annotations, selected by thresholding an annotation importance score. Importance of object annotations to VL tasks is quantified by extracting relevant semantic information from text describing the image. We show that our method is consistent and demonstrates greatly improved alignment with annotations selected by image captioning metrics and human annotation when compared against existing techniques. Lastly, we compare current detectors used in the Scene Graph Generation (SGG) benchmark as a use case, which serves as an example of when traditional object proposal evaluation techniques are misaligned(1).

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

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Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Missing Modality Robustness in Semi-Supervised Multi-Modal S...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Maheshwari, Harsh Liu, Yen-Cheng Kira, Zsolt Georgia Inst Technol Atlanta GA 30332 USA Avataar AI Bangalore Karnataka India

ISBN: (纸本)9798350318920;9798350318937

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher forMasked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 5% on robust mIoU above the most competitive baselines. Our project page is at https://***/projects/***

关键词： Algorithms Algorithms Algorithms and algorithms Datasets and evaluations formulations Image recognition and understanding Machine learning architectures

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Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data

Appearance-Based Curriculum for Semi-Supervised Learning wit...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Tanaka, Yuki Yoshida, Shuhei M. Shibata, Takashi Terao, Makoto Okatani, Takayuki Sugiyama, Masashi NEC Corp Ltd Tokyo Japan RIKEN AIP Tokyo Japan Tohoku Univ Sendai Miyagi Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

We propose an appearance-based curriculum (ABC) for a semi-supervised learning scenario where labeled images taken from limited angles and unlabeled ones taken from various angles are available for training. A common approach to semi-supervised learning relies on pseudo-labeling and data augmentation, but it struggles with large visual variations that cannot be covered by data augmentation. To solve this problem, ABC incrementally expands the pool of unlabeled images fed to a base semi-supervised learner so that newly added data are the ones most similar to those already in the pool. This way, the learner can assign pseudo-labels to the new data with high accuracy, keeping the quality of pseudo-labels higher than that when all the unlabeled data are processed at once, as customarily done in existing semi-supervised learning methods. We conducted extensive experiments and confirmed that our method outperforms the state-of-the-art semi-supervised learning methods in our scenario.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Self-Annotated 3D Geometric Learning for Smeared Points Removal

Self-Annotated 3D Geometric Learning for Smeared Points Remo...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wang, Miaowei Morris, Daniel Univ Edinburgh Edinburgh Midlothian Scotland Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350318920;9798350318937

There has been significant progress in improving the accuracy and quality of consumer-level dense depth sensors. Nevertheless, there remains a common depth pixel artifact which we call smeared points. These are points not on any 3D surface and typically occur as interpolations between foreground and background objects. As they cause fictitious surfaces, these points have the potential to harm applications dependent on the depth maps. Statistical outlier removal methods fare poorly in removing these points as they tend also to remove actual surface points. Trained network-based point removal faces difficulty in obtaining sufficient annotated data. To address this, we propose a fully self-annotated method to train a smeared point removal classifier. Our approach relies on gathering 3D geometric evidence from multiple perspectives to automatically detect and annotate smeared points and valid points. To validate the effectiveness of our method, we present a new benchmark dataset: the Real Azure-Kinect dataset. Experimental results and ablation studies show that our method outperforms traditional filters and other self-annotated methods. Our work is publicly available at https://***/wangmiaowei/wacv2024_***.

关键词： 3D computer vision Algorithms Algorithms Applications Image recognition and understanding Remote Sensing

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Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment

Learning Generalizable Perceptual Representations for Data-E...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Srinath, Suhas Mitra, Shankhanil Rao, Shika Soundararajan, Rajiv Indian Inst Sci Bengaluru 560012 India

ISBN: (纸本)9798350318920;9798350318937

No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications. A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations to train models for a target IQA application. To mitigate this requirement, there is a need for unsupervised learning of generalizable quality representations that capture diverse distortions. We enable the learning of low-level quality features agnostic to distortion types by introducing a novel quality-aware contrastive loss. Further, we leverage the generalizability of vision-language models by fine-tuning one such model to extract high-level image quality information through relevant text prompts. The two sets of features are combined to effectively predict quality by training a simple regressor with very few samples on a target dataset. Additionally, we design zero-shot quality predictions from both pathways in a completely blind setting. Our experiments on diverse datasets encompassing various distortions show the generalizability of the features and their superior performance in the data-efficient and zero-shot settings.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

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Privacy-Enhancing Person Re-identification Framework - A Dual-Stage Approach

Privacy-Enhancing Person Re-identification Framework - A Dua...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kansal, Kajal Wong, Yongkang Kankanhalli, Mohan Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9798350318920;9798350318937

In this work, we show that deep learning-based re-identification (Re-ID) models, albeit trained only with a ReID objective (i.e. if two samples belong to the same identity), encode personally identifiable information (PII) in the learned features that may lead to serious privacy concerns. In cognizance of the modern privacy regulations on protecting PII, we propose a novel dual-stage person Re-ID framework that (1) suppresses the PII from the discriminative features, and (2) introduces a controllable privacy mechanism through differential privacy. The former is achieved with a self-supervised de-identification (De-ID) decoder and an adversarial-identity (Adv-ID) module, whereas the latter mechanism leverages a controllable privacy budget to generate a privacy-protected gallery with a Gaussian noise generator. Furthermore, we introduce the notion of a privacy metric to quantify the privacy leakage in Re-ID features which is not explicitly examined in prior work. We demonstrate the feasibility of our approach in achieving a better trade-off between utility and privacy through rigorous experiments on person Re-ID benchmarks.

关键词： accountable Algorithms Algorithms Applications ethical computer vision Explainable fair Image recognition and understanding privacy-preserving Social good

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