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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

Practical Cross-modal Manifold Alignment for Robotic Grounde...

引用

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

作者： Nguyen, Andre T. Richards, Luke E. Kebe, Gaoussou Youssouf Raff, Edward Darvish, Kasra Ferraro, Frank Matuszek, Cynthia Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781665448994

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Pro-crustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.

关键词： Manifolds Measurement Learning systems computer vision conferences Natural languages Robot sensing systems

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InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

引用

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

作者： Wang, Hui-Po Orekondy, Tribhuvanesh Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781665448994

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains;(b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images and additionally increases uncertainty by 6.2x (or up to 0.85 bits) over the non-obfuscated counterparts.

关键词： Privacy Visualization Interpolation computer vision Uncertainty Target tracking conferences

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Prototype Completion with Primitive Knowledge for Few-Shot Learning

Prototype Completion with Primitive Knowledge for Few-Shot L...

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

作者： Zhang, Baoquan Li, Xutao Ye, Yunming Huang, Zhichao Zhang, Lisai Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pretraining based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful;2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes;(ii) outperforms state-of-the-art techniques by 2% similar to 9% in terms of classification accuracy. Our code is available online(1).

关键词： Knowledge engineering computer vision Codes Annotations Computational modeling Prototypes Feature extraction

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Alchemist: Parametric Control of Material Properties with Diffusion Models

Alchemist: Parametric Control of Material Properties with Di...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Prafull Sharma Varun Jampani Yuanzhen Li Xuhui Jia Dmitry Lagun Fredo Durand Bill Freeman Mark Matthews MIT CSAIL Google Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes, we generated an object-centric synthetic dataset with physically-based materials. Finetuning a modified pretrained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs.

关键词： Photorealism computer vision Text to image Neural radiance field Diffusion models Data models pattern recognition

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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

DivCo: Diverse Conditional Image Synthesis via Contrastive G...

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

作者： Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve this issue, previous works [47, 22] mainly focused on encouraging the correlation between the latent codes and their generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN [27] tried to encourage the diversity of the generated image but only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A novel latent-augmented contrastive loss is introduced, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent codes to be dissimilar. The proposed latent-augmented contrastive loss is well compatible with various cGAN architectures. Extensive experiments demonstrate that the proposed DivCo can produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple unpaired and paired image generation tasks. Training code and pretrained models are available at https://***/ruiliu-ai/DivCo.

关键词： Training Visualization computer vision Codes Correlation Image synthesis Generative adversarial networks

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引用

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

作者： Pang, Jiangmiao Qiu, Linlu Li, Xia Chen, Haofeng Li, Qi Darrell, Trevor Yu, Fisher Zhejiang Univ Hangzhou Zhejiang Peoples R China Georgia Inst Technol Atlanta GA 30332 USA Swiss Fed Inst Technol Zurich Switzerland Stanford Univ Stanford CA 94305 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.

关键词： Training Image segmentation computer vision Codes Training data Detectors Nearest neighbor methods

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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Rethinking Semantic Segmentation from a Sequence-to-Sequence...

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

作者： Zheng, Sixiao Lu, Jiachen Zhao, Hengshuang Zhu, Xiatian Luo, Zekun Wang, Yabiao Fu, Yanwei Feng, Jianfeng Xiang, Tao Torr, Philip H. S. Zhang, Li Fudan Univ Shanghai Peoples R China Univ Oxford Oxford England Univ Surrey Guildford Surrey England Tencent Youtu Lab Shanghai Peoples R China Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (i.e., without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE2OK test server leaderboard on the day of submission.

关键词： Image segmentation Visualization Semantics Transformers Decoding pattern recognition Servers

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Connecting vision and Language with Video Localized Narratives

Connecting Vision and Language with Video Localized Narrativ...

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conference on computer vision and pattern recognition (cvpr)

作者： Paul Voigtlaender Soravit Changpinyo Jordi Pont-Tuset Radu Soricut Vittorio Ferrari Google Research

We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives [36], annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://***/video-localized-narratives/

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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Localized Triplet Loss for Fine-grained Fashion Image Retrieval

Localized Triplet Loss for Fine-grained Fashion Image Retrie...

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

作者： D'Innocente, Antonio Garg, Nikhil Zhang, Yuan Bazzani, Loris Donoser, Michael Sapienza Univ Rome Rome Italy Amazon Munich Germany Amazon Seattle WA USA

ISBN: (纸本)9781665448994

Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query. However, global embeddings struggle to capture localized fine-grained similarities between images, because of aggregation operations. Our work deals with this problem by learning localized representations for fashion retrieval based on local interest points of prominent visual features specified by a user. We introduce a localized triplet loss function that compares samples based on corresponding patterns. We incorporate random local perturbation on the interest point as a key regularization technique to enforce local invariance of visual representations. Due to the absence of existing fashion datasets to train on localized representations, we introduce FashionLocalTriplets, a new high-quality dataset annotated by fashion specialists that contains triplets of women's dresses and interest points. The proposed model outperforms state-of-the-art global representations on FashionLocalTriplets.

关键词： Visualization computer vision Annotations Perturbation methods conferences Image retrieval pattern recognition

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