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NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

引用

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

作者： Hsu, Joy Mao, Jiayuan Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA USA

ISBN: (纸本)9798350301298

Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D grounded language. Hence, essential desiderata for models are to be data-efficient, generalize to different data distributions and tasks with unseen semantic forms, as well as ground complex language semantics (e.g., view-point anchoring and multi-object reference). To address these challenges, we propose NS3D, a neuro-symbolic framework for 3D grounding. NS3D translates language into programs with hierarchical structures by leveraging large language-to-code models. Different functional modules in the programs are implemented as neural networks. Notably, NS3D extends prior neuro-symbolic visual reasoning methods by introducing functional modules that effectively reason about high-arity relations (i.e., relations among more than two objects), key in disambiguating objects in complex 3D scenes. Modular and compositional architecture enables NS3D to achieve state-of-the-art results on the ReferIt3D view-dependence task, a 3D referring expression comprehension benchmark. Importantly, NS3D shows significantly improved performance on settings of data-efficiency and generalization, and demonstrate zero-shot transfer to an unseen 3D question-answering task.

关键词： language reasoning vision

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Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?

Cap4Video: What Can Auxiliary Captions Do for Text-Video Ret...

引用

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

作者： Wu, Wenhao Luo, Haipeng Fang, Bo Wang, Jingdong Ouyang, Wanli Univ Sydney Sydney NSW Australia Baidu Inc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This insight has motivated us to propose a novel approach to text-video retrieval, where we directly generate associated captions from videos using zero-shot video captioning with knowledge from web-scale pre-trained models (e.g., CLIP and GPT-2). Given the generated captions, a natural question arises: what benefits do they bring to text-video retrieval? To answer this, we introduce Cap4Video, a new framework that leverages captions in three ways: i) Input data: video-caption pairs can augment the training data. ii) Intermediate feature interaction: we perform cross-modal feature interaction between the video and caption to produce enhanced video representations. iii) Output score: the Query-Caption matching branch can complement the original Query-Video matching branch for text-video retrieval. We conduct comprehensive ablation studies to demonstrate the effectiveness of our approach. Without any post-processing, Cap4Video achieves state-of-the-art performance on four standard text-video retrieval benchmarks: MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%). The code is available at https:// ***/whwu95/Cap4Video.

关键词： and reasoning language vision

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Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Spatially Adaptive Self-Supervised Learning for Real-World I...

引用

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

作者： Li, Junyi Zhang, Zhilu Liu, Xiaoyu Feng, Chaoyu Wang, Xiaotao Lei, Lei Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://***/nagejacob/SpatiallyAdaptiveSSID.

关键词： Low-level vision

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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

Bidirectional Copy-Paste for Semi-Supervised Medical Image S...

引用

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

作者： Bai, Yunhao Chen, Duowen Li, Qingli Shen, Wei Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is avaiable at https://***/DeepMed-Lab-ECNU/BCP.

关键词： cell microscopy Medical and biological vision

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RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion

RODIN: A Generative Model for Sculpting 3D Digital Avatars U...

引用

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

作者： Wang, Tengfei Zhang, Bo Zhang, Ting Gu, Shuyang Bao, Jianmin Baltrusaitis, Tadas Shen, Jingjing Chen, Dong Wen, Fang Chen, Qifeng Guo, Baining HKUST Hong Kong Peoples R China Microsoft Res Beijing Peoples R China Intern Microsoft Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

This paper presents a 3D diffusion model that automatically generates 3D digital avatars represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is that the memory and processing costs are prohibitive for producing high-quality results with rich details. To tackle this problem, we propose the roll-out diffusion network (RODIN), which takes a 3D NeRF model represented as multiple 2D feature maps and rolls out them onto a single 2D feature plane within which we perform 3D-aware diffusion. The RODIN model brings much-needed computational efficiency while preserving the integrity of 3D diffusion by using 3D-aware convolution that attends to projected features in the 2D plane according to their original relationships in 3D. We also use latent conditioning to orchestrate the feature generation with global coherence, leading to high-fidelity avatars and enabling semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair. We also demonstrate 3D avatar generation from image or text, as well as text-guided editability.

关键词： vision + graphics

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Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions

Learning Weather-General and Weather-Specific Features for I...

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

作者： Zhu, Yurui Wang, Tianyu Fu, Xueyang Yang, Xuanyu Guo, Xin Dai, Jifeng Qiao, Yu Hu, Xiaowei Univ Sci & Technol China Langfang Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using a single set of network parameters. In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmarks. Codes and datasets are available at this repository.

关键词： Low-level vision

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A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift

A Simple Baseline for Video Restoration with Grouped Spatial...

引用

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

作者： Li, Dasong Shi, Xiaoyu Zhang, Yi Cheung, Ka Chun See, Simon Wang, Xiaogang Qin, Hongwei Li, Hongsheng CUHK MMLab Hong Kong Peoples R China NVIDIA AI Technol Ctr Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China CPII InnoHK Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame information. However, existing deep learning methods often rely on complicated network architectures, such as optical flow estimation, deformable convolution, and cross-frame self-attention layers, resulting in high computational costs. In this study, we propose a simple yet effective framework for video restoration. Our approach is based on grouped spatial-temporal shift, which is a lightweight and straightforward technique that can implicitly capture inter-frame correspondences for multi-frame aggregation. By introducing grouped spatial shift, we attain expansive effective receptive fields. Combined with basic 2D convolution, this simple framework can effectively aggregate inter-frame information. Extensive experiments demonstrate that our framework outperforms the previous state-of-the-art method, while using less than a quarter of its computational cost, on both video deblurring and video denoising tasks. These results indicate the potential for our approach to significantly reduce computational overhead while maintaining high-quality results. Code is avaliable at https://***/dasongli1/Shift-Net.

关键词： Low-level vision

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Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection

Consistent-Teacher: Towards Reducing Inconsistent Pseudo-tar...

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

作者： Wang, Xinjiang Yang, Xingyi Zhang, Shilong Li, Yijiang Feng, Litong Fang, Shijie Lyu, Chengqi Chen, Kai Zhang, Wayne SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Peking Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment (ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo-bounding boxes. Then we calibrate the subtask predictions by designing a 3D feature alignment module (FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of pseudo-b-boxes, which stabilizes the number of ground truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.7 mAP. Our code is available at https://github. com/Adamdad/ConsistentTeacher.

关键词： vision applications and systems

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DATE: Domain Adaptive Product Seeker for E-commerce

DATE: Domain Adaptive Product Seeker for E-commerce

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

作者： Li, Haoyuan Jiang, Hao Jin, Tao Li, Mengyan Chen, Yan Lin, Zhijie Zhao, Yang Zhao, Zhou Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Product Retrieval (PR) and Grounding (PG), aiming to seek image and object-level products respectively according to a textual query, have attracted great interest recently for better shopping experience. Owing to the lack of relevant datasets, we collect two large-scale benchmark datasets from Taobao Mall and Live domains with about 474k and 101k image-query pairs for PR, and manually annotate the object bounding boxes in each image for PG. As annotating boxes is expensive and time-consuming, we attempt to transfer knowledge from annotated domain to unannotated for PG to achieve un-supervised Domain Adaptation (PG-DA). We propose a Domain Adaptive Product Seeker (DATE) framework, regarding PR and PG as Product Seeking problem at different levels, to assist the query date the product. Concretely, we first design a semantics-aggregated feature extractor for each modality to obtain concentrated and comprehensive features for following efficient retrieval and fine-grained grounding tasks. Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG. Besides, we devise a domain aligner for PG-DA to alleviate uni-modal marginal and multi-modal conditional distribution shift between source and target domains, and design a pseudo box generator to dynamically select reliable instances and generate bounding boxes for further knowledge transfer. Extensive experiments show that our DATE achieves satisfactory performance in fully-supervised PR, PG and un-supervised PG-DA. Our desensitized datasets will be publicly available here(1).

关键词： detection recognition: Categorization retrieval

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DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

DINN360: Deformable Invertible Neural Network for Latitude-a...

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

作者： Guo, Yichen Xu, Mai Jiang, Lai Sigal, Leonid Chen, Yunjin Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (纸本)9798350301298

With the rapid development of virtual reality, 360 degrees images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360 degrees images. To alleviate this issue, we propose the first attempt at 360 degrees image rescaling, which refers to downscaling a 360 degrees image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360 degrees image given the LR variant. Specifically, we first analyze two 360 degrees image datasets and observe several findings that characterize how 360 degrees images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360 degrees image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2x, 4x and 8x 360 degrees image rescaling.

关键词： Low-level vision

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