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GANmut: Learning Interpretable Conditional Space for Gamut of Emotions

GANmut: Learning Interpretable Conditional Space for Gamut o...

引用

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

作者： d'Apolito, Stefano Paudel, Danda Pani Huang, Zhiwu Romero, Andres Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven PSI Leuven Belgium

ISBN: (纸本)9781665445092

Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labelling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework that learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on handcrafted labels. Our framework only uses the categorical labels of basic emotions to learn jointly the conditional space as well as emotion manipulation. Such learning can benefit from the image variability within discrete labels, especially when the intrinsic labels reside beyond the discrete space of the defined. Our experiments demonstrate the effectiveness of the proposed framework, by allowing us to control and generate a gamut of complex and compound emotions while using only the basic categorical emotion labels during training.

关键词： Training computer vision Image synthesis Computational modeling Aerospace electronics Search problems pattern recognition

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Where and What? Examining Interpretable Disentangled Representations

Where and What? Examining Interpretable Disentangled Represe...

引用

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

作者： Zhu, Xinqi Xu, Chang Tao, Dacheng Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement.

关键词： computer vision Codes Image coding Perturbation methods Computational modeling Encoding pattern recognition

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Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling

Improving Multiple Pedestrian Tracking by Track Management a...

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

作者： Stadler, Daniel Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning Karlsruhe Germany

ISBN: (纸本)9781665445092

Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to reidentify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previously found tracks. Since those detections can have substantial overlaps with nearby targets especially in crowded scenarios - the extracted features are insufficient for a reliable re-identification. In contrast, we propose a novel occlusion handling strategy that explicitly models the relation between occluding and occluded tracks outperforming the feature-based approach, while not depending on a separate re-identification network. Furthermore, we improve the track management of a regressionbased method in order to bypass missing detections and to deal with tracks leaving the scene at the border of the image. Finally, we apply our tracker in both temporal directions and merge tracklets belonging to the same target, which further enhances the performance. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three popular pedestrian tracking benchmarks MOT16, MOT17, and MOT20.

关键词： Visualization computer vision Target tracking Benchmark testing Feature extraction pattern recognition Reliability

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Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Revamping Cross-Modal Recipe Retrieval with Hierarchical Tra...

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

作者： Salvador, Amaia Gundogdu, Erhan Bazzani, Loris Donoser, Michael Amazon Seattle WA 98109 USA

ISBN: (纸本)9781665445092

Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing encoders for text and images. We introduce a hierarchical recipe Transformer which attentively encodes individual recipe components (titles, ingredients and instructions). Further, we propose a self-supervised loss function computed on top of pairs of individual recipe components, which is able to leverage semantic relationships within recipes, and enables training using both image-recipe and recipe-only samples. We conduct a thorough analysis and ablation studies to validate our design choices. As a result, our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset. We make code and models publicly available(1).

关键词： Training computer vision Codes Computational modeling Semantics Machine learning Transformers

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Capsule Network is Not More Robust than Convolutional Network

Capsule Network is Not More Robust than Convolutional Networ...

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

作者： Gu, Jindong Tresp, Volker Hu, Han Univ Munich Munich Germany Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet affect its robustness. In this paper, we first carefully examine the special designs in CapsNet that differ from that of a ConvNet commonly used for image classification. The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization. Along with these major differences, we conduct comprehensive ablation studies on three kinds of robustness, including affine transformation, overlapping digits, and semantic representation. The study reveals that some designs, which are thought critical to CapsNet, actually can harm its robustness, i.e., the dynamic routing layer and the transformation process, while others are beneficial for the robustness. Based on these findings, we propose enhanced ConvNets simply by introducing the essential components behind the CapsNet's success. The proposed simple ConvNets can achieve better robustness than the CapsNet.

关键词： computer vision Aggregates Semantics Routing Robustness pattern recognition Task analysis

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Hyperdimensional computing as a framework for systematic aggregation of image descriptors

Hyperdimensional computing as a framework for systematic agg...

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

作者： Neubert, Peer Schubert, Stefan Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781665445092

Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and > 2x better worst-case performance compared to runner-up.

关键词： computer vision Systematics Aggregates Semantics Memory management Layout Tools

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Collaborative Static and Dynamic vision-Language Streams for Spatio-Temporal Video Grounding

Collaborative Static and Dynamic Vision-Language Streams for...

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conference on computer vision and pattern recognition (cvpr)

作者： Zihang Lin Chaolei Tan Jian-Fang Hu Zhi Jin Tiancai Ye Wei-Shi Zheng Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Tencent China

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatiotemporal visuallinguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intraframe visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall frame-work achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.

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Improving Table Structure recognition with Visual-Alignment Sequential Coordinate Modeling

Improving Table Structure Recognition with Visual-Alignment ...

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conference on computer vision and pattern recognition (cvpr)

作者： Yongshuai Huang Ning Lu Dapeng Chen Yibo Li Zecheng Xie Shenggao Zhu Liangcai Gao Wei Peng Huawei Technologies Ltd. Peking University

Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, the previous methods struggle with imprecise bounding boxes as the logical representation lacks local visual information. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

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conference on computer vision and pattern recognition (cvpr)

作者： Marius Memmel Roman Bachmann Amir Zamir University of Washington Swiss Federal Institute of Technology (EPFL)

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

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No frame left behind: Full Video Action recognition

No frame left behind: Full Video Action Recognition

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

作者： Liu, Xin Pintea, Silvia L. Nejadasl, Fatemeh Karimi Booij, Olaf van Gemert, Jan C. Delft Univ Technol Comp Vis Lab Delft Netherlands TomTom Amsterdam Netherlands

ISBN: (纸本)9781665445092

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.

关键词： Training computer vision Philosophical considerations Semantics Memory management Sampling methods Nonhomogeneous media

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