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Aligning Bag of Regions for Open-Vocabulary Object Detection

Aligning Bag of Regions for Open-Vocabulary Object Detection

引用

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

作者： Wu, Size Zhang, Wenwei Jin, Sheng Liu, Wentao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Univ Hong Kong Hong Kong Peoples R China SenseTime Res & Tetras AI Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic concepts. However, existing open-vocabulary object detectors only align region embeddings individually with the corresponding features extracted from the VLMs. Such a design leaves the compositional structure of semantic concepts in a scene under-exploited, although the structure may be implicitly learned by the VLMs. In this work, we propose to align the embedding of bag of regions beyond individual regions. The proposed method groups contextually interrelated regions as a bag. The embeddings of regions in a bag are treated as embeddings of words in a sentence, and they are sent to the text encoder of a VLM to obtain the bag-of-regions embedding, which is learned to be aligned to the corresponding features extracted by a frozen VLM. Applied to the commonly used Faster R-CNN, our approach surpasses the previous best results by 4.6 box AP50 and 2.8 mask AP on novel categories of open-vocabulary COCO and LVIS benchmarks, respectively. Code and models are available at https://***/wusize/ovdet.

关键词： detection recognition: Categorization retrieval

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DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

DANI-Net: Uncalibrated Photometric Stereo by Differentiable ...

引用

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

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Zhejiang Univ State Key Lab Brain Machine Intelligence Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol I Iangzhou Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.

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

来源：评论

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High-fidelity 3D Face Generation from Natural Language Descriptions

High-fidelity 3D Face Generation from Natural Language Descr...

引用

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

作者： Wu, Menghua Zhu, Hao Huang, Linjia Zhuang, Yiyu Lu, Yuanxun Cao, Xun Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9798350301298

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build DESCRIBE3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a two-stage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and DESCRIBE3D dataset are released at https://***/zhuhao-nju/describe3d.

关键词： and reasoning language 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...

引用

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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High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

引用

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

作者： Han, Jin Asano, Yuta Shi, Boxin Zheng, Yinqiang Sato, Imari Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Natl Inst Informat Tokyo Japan Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

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

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Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

Contrastive Semi-supervised Learning for Underwater Image Re...

引用

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

作者： Huang, Shirui Wang, Keyan Liu, Huan Chen, Jun Li, Yunsong Xidian Univ Xian Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network training. However, the naive mean-teacher method suffers from two main problems: (1) The consistency loss used in training might become ineffective when the teacher's prediction is wrong. (2) Using L1 distance may cause the network to overfit wrong labels, resulting in confirmation bias. To address the above problems, we first introduce a reliable bank to store the "best-ever" outputs as pseudo ground truth. To assess the quality of outputs, we conduct an empirical analysis based on the monotonicity property to select the most trustworthy NR-IQA method. Besides, in view of the confirmation bias problem, we incorporate contrastive regularization to prevent the overfitting on wrong labels. Experimental results on both full-reference and non-reference underwater benchmarks demonstrate that our algorithm has obvious improvement over SOTA methods quantitatively and qualitatively. Code has been released at https://***/Huang-ShiRui/Semi-UIR.

关键词： Low-level vision

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Rethinking Few-Shot Medical Segmentation: A Vector Quantization View

Rethinking Few-Shot Medical Segmentation: A Vector Quantizat...

引用

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

作者： Huang, Shiqi Xu, Tingfa Shen, Ning Mu, Feng Li, Jianan Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance. This phenomenon can be theoretically interpreted in Vector Quantization (VQ) view: the more prototypes, the more clusters are separated from pixel-wise feature points distributed over the full space. However, as we further think about few-shot segmentation with this perspective, it is found that the clusterization of feature points and the adaptation to unseen tasks have not received enough attention. Motivated by the observation, we propose a learning VQ mechanism consisting of grid-format VQ (GFVQ), self-organized VQ (SOVQ) and residual oriented VQ (ROVQ). To be specific, GFVQ generates the prototype matrix by averaging square grids over the spatial extent, which uniformly quantizes the local details;SOVQ adaptively assigns the feature points to different local classes and creates a new representation space where the learnable local prototypes are updated with a global view;ROVQ introduces residual information to fine-tune the aforementioned learned local prototypes without re-training, which benefits the generalization performance for the irrelevance to the training task. We empirically show that our VQ framework yields the state-of-the-art performance over abdomen, cardiac and prostate MRI datasets and expect this work will provoke a rethink of the current few-shot medical segmentation model design. Our code will soon be publicly available.

关键词： cell microscopy Medical and biological vision

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Visual recognition-Driven Image Restoration for Multiple Degradation with Intrinsic Semantics Recovery

Visual Recognition-Driven Image Restoration for Multiple Deg...

引用

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

作者： Yang, Zizheng Huang, Jie Chang, Jiahao Zhou, Man Yu, Hu Zhang, Jinghao Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

关键词： Low-level vision

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Knowledge Combination to Learn Rotated Detection Without Rotated Annotation

Knowledge Combination to Learn Rotated Detection Without Rot...

引用

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

作者： Zhu, Tianyu Ferenczi, Bryce Purkait, Pulak Drummond, Tom Rezatofighi, Hamid van den Hengel, Anton Amazon IML Seattle WA 98109 USA Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350301298

Rotated bounding boxes drastically reduce output ambiguity of elongated objects, making it superior to axis-aligned bounding boxes. Despite the effectiveness, rotated detectors are not widely employed. Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead. In this paper, we propose a framework that allows the model to predict precise rotated boxes only requiring cheaper axis-aligned annotation of the target dataset(1). To achieve this, we leverage the fact that neural networks are capable of learning richer representation of the target domain than what is utilized by the task. The under-utilized representation can be exploited to address a more detailed task. Our framework combines task knowledge of an out-of-domain source dataset with stronger annotation and domain knowledge of the target dataset with weaker annotation. A novel assignment process and projection loss are used to enable the co-training on the source and target datasets. As a result, the model is able to solve the more detailed task in the target domain, without additional computation overhead during inference. We extensively evaluate the method on various target datasets including fresh-produce dataset, HRSC2016 and SSDD. Results show that the proposed method consistently performs on par with the fully supervised approach.

关键词： detection recognition: Categorization retrieval

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Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Unsupervised Intrinsic Image Decomposition with LiDAR Intens...

引用

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

作者： Sato, Shogo Yao, Yasuhiro Yoshida, Taiga Kaneko, Takuhiro Ando, Shingo Shimamura, Jun INTT Human Informat Labs Tokyo Japan NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9798350301298

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.

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

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