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Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling

Neural Points: Point Cloud Representation with Neural Fields...

引用

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

作者： Feng, Wanquan Li, Jin Cai, Hongrui Luo, Xiaonan Zhang, Juyong Univ Sci & Technol China Hefei Peoples R China Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose Neural Points, a novel point cloud representation and apply it to the arbitrary factored upsampling task. Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields. Therefore, Neural Points contain more shape information and thus have a stronger representation ability. Neural Points is trained with surface containing rich geometric details, such that the trained model has enough expression ability for various shapes. Specifically, we extract deep local features on the points and construct neural fields through the local isomorphism between the 2D parametric domain and the 3D local patch. In the final, local neural fields are integrated together to form the global surface. Experimental results show that Neural Points has powerful representation ability and demonstrate excellent robustness and generalization ability. With Neural Points, we can resample point cloud with arbitrary resolutions, and it outperforms the state-of-the-art point cloud upsampling methods. Code is available at https://***/WanquanF/NeuralPoints.

关键词： Point cloud compression computer vision Three-dimensional displays Codes Shape Feature extraction Robustness

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AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

AxIoU: An Axiomatically Justified Measure for Video Moment R...

引用

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

作者： Togashi, Riku Otani, Mayu Nakashima, Yuta Rahtu, Esa Heikkila, Janne Sakai, Tetsuya Waseda Univ Cyberagent Inc Tokyo Japan Cyberagent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland Waseda Univ Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@ K, theta for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarizes the Intersection over Union (IoU) of each retrieved video moment using the threshold theta and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@ K, theta satisfies the first axiom only. We also empirically examine how AxIoU agrees with R@ K, theta, as well as its stability with respect to change in the test data and human-annotated temporal boundaries.

关键词： computer vision Current measurement Computational modeling Stability analysis pattern recognition Reliability

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Visual Program Distillation: Distilling Tools and Programmatic Reasoning into vision-Language Models

Visual Program Distillation: Distilling Tools and Programmat...

引用

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

作者： Hu, Yushi Stretcu, Otilia Lu, Chun-Ta Viswanathan, Krishnamurthy Hata, Kenji Luo, Enming Krishna, Ranjay Fuxman, Ariel Google Res Mountain View CA 94043 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350353006

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.

关键词： cross-modal reasoning distillation instruction tuning large language model multimodal synthetic data tools vision-language model visual programming

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Semantic hierarchies for visual object recognition

Semantic hierarchies for visual object recognition

引用

ieee conference on computer vision and pattern recognition

作者： Marszalek, Marcin Schmid, Cordelia LEAR LJK INRIA 665 Europe F-38330 Montbonnot St Martin France

ISBN: (纸本)9781424411795

In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic hierarchy of discriminative classifiers and how to use it to perform object detection. We evaluate how our approach influences the classification accuracy and speed on the PASCAL VOC challenge 2006 dataset, a set of challenging real-world images. We also demonstrate additional features that become available to object recognition due to the extension with semantic inference tools-we can classify high-level categories, such as animals, and we can train part detectors, for example a window detector, by pure inference in the semantic network.

关键词： Object recognition

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StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retriev...

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

作者： Sain, Aneeshan Bhunia, Ayan Kumar Yang, Yongxin Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England

ISBN: (纸本)9781665445092

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to metatrain our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR.

关键词： Adaptation models computer vision Codes Computational modeling Semantics Image retrieval pattern recognition

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See through Gradients: Image Batch Recovery via GradInversion

See through Gradients: Image Batch Recovery via GradInversio...

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

作者： Yin, Hongxu Mallya, Arun Vahdat, Arash Alvarez, Jose M. Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781665445092

Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possibility of recovering input data given gradients under very restrictive conditions - a single input point, or a network with no non-linearities, or a small 32 x 32 px input batch. Therefore, averaging gradients over larger batches was thought to be safe. In this work, we introduce GradInversion, using which input images from a larger batch (8 - 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224 x 224 px). We formulate an optimization task that converts random noise into natural images, matching gradients while regularizing image fidelity. We also propose an algorithm for target class label recovery given gradients. We further propose a group consistency regularization framework, where multiple agents starting from different random seeds work together to find an enhanced reconstruction of the original data batch. We show that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes.

关键词： Training Deep learning Data privacy Image matching Estimation Collaborative work pattern recognition

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Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise

Learning An Explicit Weighting Scheme for Adapting Complex H...

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

作者： Rui, Xiangyu Cao, Xiangyong Xie, Qi Yue, Zongsheng Zhao, Qian Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665445092

An efficient approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements. Such weighting scheme, however, largely depends on the prior understanding or subjective distribution assumption on HSI noises, making them easily biased to complicated real noises, and hardly generalizable to diverse practical scenarios. Against this issue, this paper proposes a new scheme aiming to capture general weighting principle in a data-driven manner. Specifically, such weighting principle is delivered by an explicit function, called hyperweight-net (HWnet), mapping from an input noisy image to its properly imposed weights. A Bayesian framework as well as a variational inference algorithm for inferring HWnet parameters is elaborately designed, expecting to extract the latent weighting rule for general diverse and complicated noisy HSIs. Comprehensive experiments substantiate that the learned HWnet can be not only finely generalized to different noise types from those used in training, but also effectively transferred to other weighted models. Besides, as a sounder guidance, HWnet can help to more faithfully and robustly achieve deep hyperspectral prior(DHP). The extracted weights by HWnet are verified to be able to effectively capture complex noise knowledge underlying input HSI, revealing its working insight in experiments.

关键词： Training Adaptation models computer vision Computational modeling Noise reduction Inference algorithms pattern recognition

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Optimal Correction Cost for Object Detection Evaluation

Optimal Correction Cost for Object Detection Evaluation

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

作者： Otani, Mayu Togashi, Riku Nakashima, Yuta Rahtu, Esa Heikkila, Janne Satoh, Shin'ichi CyberAgent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms of the performance of ranked instance retrieval. Such the assumption for the evaluation task does not suit some downstream tasks. To alleviate the gap between downstream tasks and the evaluation scenario, we propose Optimal Correction Cost (OC-cost), which assesses detection accuracy at image level. OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy. The cost is obtained by solving an optimal transportation problem between the detections and the ground truths. Unlike mAP, OC-cost is designed to penalize false positive and false negative detections properly, and every image in a dataset is treated equally. Our experimental result validates that OC-cost has better agreement with human preference than a ranking-based measure, i.e., mAP for a single image. We also show that detectors' rankings by OC-cost are more consistent on different data splits than mAP. Our goal is not to replace mAP with OC-cost but provide an additional tool to evaluate detectors from another aspect. To help future researchers and developers choose a target measure, we provide a series of experiments to clarify how mAP and OC-cost differ.

关键词： computer vision Costs Layout Transportation Detectors Object detection pattern recognition

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Bi-GCN: Binary Graph Convolutional Network

Bi-GCN: Binary Graph Convolutional Network

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

作者： Wang, Junfu Wang, Yunhong Yang, Zhen Yang, Liang Guo, Yuanfang Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781665445092

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of similar to 30x for both the network parameters and input data, and accelerate the inference speed by an average of similar to 47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.

关键词： computer vision Memory management Loading Graph neural networks pattern recognition Task analysis

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BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision

BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...

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

作者： Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

ISBN: (纸本)9781665448994

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.

关键词： Training computer vision Convolution conferences Neural networks Memory management Decoding

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