pwc-archive/papers-with-abstracts huggingface dataset:
Bilateral Video Magnification Filter
Authors: Shoichiro Takeda, Kenta Niwa, Mariko Isogawa, Shinya Shimizu, Kazuki Okami, Yushi Aono
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Unity

    Eulerian video magnification (EVM) has progressed to magnify subtle motions with a target frequency even under the presence of large motions of objects. However, existing EVM methods often fail to produce desirable results in real videos due to (1) mis-extracting subtle motions with a non-target frequency and (2) collapsing results when large de/acceleration motions occur (e.g., objects suddenly start, stop, or change direction). To enhance EVM performance on real videos, this paper proposes a bilateral video magnification filter (BVMF) that offers simple yet robust temporal filtering. BVMF has two kernels; (I) one kernel performs temporal bandpass filtering via a Laplacian of Gaussian whose passband peaks at the target frequency with unity gain and (II) the other kernel excludes large motions outside the magnitude of interest by Gaussian filtering on the intensity of the input signal via the Fourier shift theorem. Thus, BVMF extracts only subtle motions with the target frequency while excluding large motions outside the magnitude of interest, regardless of motion dynamics. In addition, BVMF runs the two kernels in the temporal and intensity domains simultaneously like the bilateral filter does in the spatial and intensity domains. This simplifies implementation and, as a secondary effect, keeps the memory usage low. Experiments conducted on synthetic and real videos show that BVMF outperforms state-of-the-art methods.
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation
Authors: Andra Petrovai, Sergiu Nedevschi
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Depth Estimation, Monocular Depth Estimation, Self-Supervised Learning

    We present a novel self-distillation based self-supervised monocular depth estimation (SD-SSMDE) learning framework. In the first step, our network is trained in a self-supervised regime on high-resolution images with the photometric loss. The network is further used to generate pseudo depth labels for all the images in the training set. To improve the performance of our estimates, in the second step, we re-train the network with the scale invariant logarithmic loss supervised by pseudo labels. We resolve scale ambiguity and inter-frame scale consistency by introducing an automatically computed scale in our depth labels. To filter out noisy depth values, we devise a filtering scheme based on the 3D consistency between consecutive views. Extensive experiments demonstrate that each proposed component and the self-supervised learning framework improve the quality of the depth estimation over the baseline and achieve state-of-the-art results on the KITTI and Cityscapes datasets.
Towards Multi-Domain Single Image Dehazing via Test-Time Training
Authors: Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, Keyan Wang
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Dehazing, Meta-Learning, Single Image Dehazing

    Recent years have witnessed significant progress in the area of single image dehazing, thanks to the employment of deep neural networks and diverse datasets. Most of the existing methods perform well when the training and testing are conducted on a single dataset. However, they are not able to handle different types of hazy images using a dehazing model trained on a particular dataset. One possible remedy is to perform training on multiple datasets jointly. However, we observe that this training strategy tends to compromise the model performance on individual datasets. Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest. Specifically, during the test time, the helper network evaluates the quality of the dehazing results, then directs the dehazing network to improve the quality by adjusting its parameters via self-supervision. Nevertheless, the inclusion of the helper network does not automatically ensure the desired performance improvement. For this reason, a meta-learning approach is employed to make the objectives of the dehazing and helper networks consistent with each other. We demonstrate the effectiveness of the proposed method by providing extensive supporting experiments.
Mutual Quantization for Cross-Modal Search With Noisy Labels
Authors: Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Quantization

    Deep cross-modal hashing has become an essential tool for supervised multimodal search. These models tend to be optimized with large, curated multimodal datasets, where most labels have been manually verified. Unfortunately, in many scenarios, such accurate labeling may not be available. In contrast, datasets with low-quality annotations may be acquired, which inevitably introduce numerous mistakes or label noise and therefore degrade the search performance. To address the challenge, we present a general robust cross-modal hashing framework to correlate distinct modalities and combat noisy labels simultaneously. More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities jointly with small-loss samples that are selected with the PC loss and a mutual quantization loss. The small-loss sample selection from such joint loss can help choose confident examples to guide the model training, and the mutual quantization loss can maximize the agreement between different modalities and is beneficial to improve the effectiveness of sample selection. Experiments on three widely-used multimodal datasets show that our method significantly outperforms existing state-of-the-art methods.
Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition
Authors: Hongji Guo, Hanjing Wang, Qiang Ji
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Action Recognition, Prediction

    A complex action consists of a sequence of atomic actions that interact with each other over a relatively long period of time. This paper introduces a probabilistic model named Uncertainty-Guided Probabilistic Transformer (UGPT) for complex action recognition. The self-attention mechanism of a Transformer is used to capture the complex and long-term dynamics of the complex actions. By explicitly modeling the distribution of the attention scores, we extend the deterministic Transformer to a probabilistic Transformer in order to quantify the uncertainty of the prediction. The model prediction uncertainty is used to improve both training and inference. Specifically, we propose a novel training strategy by introducing a majority model and a minority model based on the epistemic uncertainty. During the inference, the prediction is jointly made by both models through a dynamic fusion strategy. Our method is validated on the benchmark datasets, including Breakfast Actions, MultiTHUMOS, and Charades. The experiment results show that our model achieves the state-of-the-art performance under both sufficient and insufficient data.
Learning Semantic Associations for Mirror Detection
Authors: Huankang Guan, Jiaying Lin, Rynson W.H. Lau
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Segmentation, Mirror Detection

    Mirrors generally lack a consistent visual appearance, making mirror detection very challenging. Although recent works that are based on exploiting contextual contrasts and corresponding relations have achieved good results, heavily relying on contextual contrasts and corresponding relations to discover mirrors tend to fail in complex real-world scenes, where a lot of objects, e.g., doorways, may have similar features as mirrors. We observe that humans tend to place mirrors in relation to certain objects for specific functional purposes, e.g., a mirror above the sink. Inspired by this observation, we propose a model to exploit the semantic associations between the mirror and its surrounding objects for a reliable mirror localization. Our model first acquires class-specific knowledge of the surrounding objects via a semantic side-path. It then uses two novel modules to exploit semantic associations: 1) an Associations Exploration (AE) Module to extract the associations of the scene objects based on fully connected graph models, and 2) a Quadruple-Graph (QG) Module to facilitate the diffusion and aggregation of semantic association knowledge using graph convolutions. Extensive experiments show that our method outperforms the existing methods and sets the new state-of-the-art on both PMD dataset (f-measure: 0.844) and MSD dataset (f-measure: 0.889).
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation
Authors: Zhaoyang Zeng, Yongsheng Luo, Zhenhua Liu, Fengyun Rao, Dian Li, Weidong Guo, Zhen Wen
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, Recommendation Systems, speech-recognition, Speech Recognition, Video Similarity

    Multi-modal video similarity evaluation is important for video recommendation systems such as video de-duplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://tencent-mvse.github.io/.
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis
Authors: Tianyi Chen, Yunfei Zhang, Xiaoyang Huo, Si Wu, Yong Xu, Hau San Wong
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Generative Adversarial Network, Image Generation

    Generative Adversarial Network (GAN)-based models have greatly facilitated image synthesis. However, the model performance may be degraded when applied to fine-grained data, due to limited training samples and subtle distinction among categories. Different from generic GANs, we address the issue from a new perspective of discovering and utilizing the underlying structure of real data to explicitly regularize the spatial organization of latent space. To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN. By projecting random vectors drawn from a prior distribution onto a hyper-sphere, we can model more complex distributions, while at the same time the similarity between the resulting latent vectors depends only on the angle, but not on their magnitudes. On the other hand, we also incorporate a mapping network to map real images onto the hyper-sphere, and match latent vectors with the underlying structure of real data via real-fake cluster alignment. As a result, we obtain a spatially organized latent space, which is useful for capturing class-independent variation factors. The experimental results suggest that our SphericGAN achieves state-of-the-art performance in synthesizing high-fidelity images with precise class semantics.
Contextual Similarity Distillation for Asymmetric Image Retrieval
Authors: Hui Wu, Min Wang, Wengang Zhou, Houqiang Li, Qi Tian
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Retrieval, Retrieval

    Asymmetric image retrieval, which typically uses small model for query side and large model for database server, is an effective solution for resource-constrained scenarios. However, existing approaches either fail to achieve feature coherence or make strong assumptions, e.g., requiring labeled datasets or classifiers from large model, etc., which limits their practical application. To this end, we propose a flexible contextual similarity distillation framework to enhance the small query model and keep its output feature compatible with that of large gallery model, which is crucial with asymmetric retrieval. In our approach, we learn the small model with a new contextual similarity consistency constraint without any data label. During the small model learning, it preserves the contextual similarity among each training image and its neighbors with the features extracted by the large model. Note that this simple constraint is consistent with simultaneous first-order feature vector preserving and second-order ranking list preserving. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching
Authors: Donghun Kang, Hyeonjoong Jang, Jungeon Lee, Chong-Min Kyung, Min H. Kim
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: ARC, Stereo Matching

    Omnidirectional cameras have been used widely to better understand surrounding environments. They are often configured as stereo to estimate depth. However, due to the optics of the fisheye lens, conventional epipolar geometry is inapplicable directly to omnidirectional camera images. Intermediate formats of omnidirectional images, such as equirectangular images, have been used. However, stereo matching performance on these image formats has been lower than the conventional stereo due to severe image distortion near pole regions. In this paper, to address the distortion problem of omnidirectional images, we devise a novel subdivision scheme of a spherical geodesic grid. This enables more isotropic patch sampling of spherical image information in the omnidirectional camera space. Our spherical geodesic grid is tessellated with an equal-arc subdivision, making the cell sizes and in-between distances as uniform as possible, i.e., the arc length of the spherical grid cell's edges is well regularized. Also, our uniformly tessellated coordinates in a 2D image can be transformed into spherical coordinates via one-to-one mapping, allowing for analytical forward/backward transformation. Our uniform tessellation scheme achieves a higher accuracy of stereo matching than the traditional cylindrical and cubemap-based approaches, reducing the memory footage required for stereo matching by 20 %.
DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image Synthesis
Authors: Jia-Wei Chen, Chia-Mu Yu, Ching-Chia Kao, Tzai-Wei Pang, Chun-Shien Lu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Generation

    Despite an increased demand for valuable data, the privacy concerns associated with sensitive datasets present a barrier to data sharing. One may use differentially private generative models to generate synthetic data. Unfortunately, generators are typically restricted to generating images of low-resolutions due to the limitation of noisy gradients. Here, we propose DPGEN, a network model designed to synthesize high-resolution natural images while satisfying differential privacy. In particular, we propose an energy-guided network trained on sanitized data to indicate the direction of the true data distribution via Langevin Markov chain Monte Carlo (MCMC) sampling method. In contrast to the state-of-the-art methods that can process only low-resolution images (e.g., MNIST and Fashion-MNIST), DPGEN can generate differentially private synthetic images with resolutions up to 128*128 with superior visual quality and data utility. Our code is available at https://github.com/chiamuyu/DPGEN
SMPL-A: Modeling Person-Specific Deformable Anatomy
Authors: Hengtao Guo, Benjamin Planche, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Anatomy, Diagnostic, Human Mesh Recovery

    A variety of diagnostic and therapeutic protocols rely on locating in vivo target anatomical structures, which can be obtained from medical scans. However, organs move and deform as the patient changes his/her pose. In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e.g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment. Such custom proxy methods are typically sub-optimal, constraining the clinicians and costing precious time and money to the patients. To the best of our knowledge, this work is the first to present a learning-based approach to estimate the patient's internal organ deformation for arbitrary human poses in order to assist with radiotherapy and similar medical protocols. The underlying method first leverages medical scans to learn a patient-specific representation that potentially encodes the organ's shape and elastic properties. During inference, given the patient's current body pose information and the organ's representation extracted from previous medical scans, our method can estimate their current organ deformation to offer guidance to clinicians. We conduct experiments on a well-sized dataset which is augmented through real clinical data using finite element modeling. Our results suggest that pose-dependent organ deformation can be learned through a point cloud autoencoder conditioned on the parametric pose input. We hope that this work can be a starting point for future research towards closing the loop between human mesh recovery and anatomical reconstruction, with applications beyond the medical domain.
Contextual Outpainting With Object-Level Contrastive Learning
Authors: Jiacheng Li, Chang Chen, Zhiwei Xiong
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Contrastive Learning, Image Outpainting, Object

    We study the problem of contextual outpainting, which aims to hallucinate the missing background contents based on the remaining foreground contents. Existing image outpainting methods focus on completing object shapes or extending existing scenery textures, neglecting the semantically meaningful relationship between the missing and remaining contents. To explore the semantic cues provided by the remaining foreground contents, we propose a novel ConTextual Outpainting GAN (CTO-GAN), leveraging the semantic layout as a bridge to synthesize coherent and diverse background contents. To model the contextual correlation between foreground and background contents, we incorporate an object-level contrastive loss to regularize the learning of cross-modal representations of foreground contents and the corresponding background semantic layout, facilitating accurate semantic reasoning. Furthermore, we improve the realism of the generated background contents via detecting generated context in adversarial training. Extensive experiments demonstrate that the proposed method achieves superior performance compared with existing solutions on the challenging COCO-stuff dataset.
Neural Architecture Search With Representation Mutual Information
Authors: Xiawu Zheng, Xiang Fei, Lei Zhang, Chenglin Wu, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Rongrong Ji
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Neural Architecture Search

    Performance evaluation strategy is one of the most important factors that determine the effectiveness and efficiency in Neural Architecture Search (NAS). Existing strategies, such as employing standard training or performance predictor, often suffer from high computational complexity and low generality. To address this issue, we propose to rank architectures by Representation Mutual Information (RMI). Specifically, given an arbitrary architecture that has decent accuracy, architectures that have high RMI with it always yield good accuracies. As an accurate performance indicator to facilitate NAS, RMI not only generalizes well to different search spaces, but is also efficient enough to evaluate architectures using only one batch of data. Building upon RMI, we further propose a new search algorithm termed RMI-NAS, facilitating with a theorem to guarantee the global optimal of the searched architecture. In particular, RMI-NAS first randomly samples architectures from the search space, which are then effectively classified as positive or negative samples by RMI. We then use these samples to train a random forest to explore new regions, while keeping track of the distribution of positive architectures. When the sample size is sufficient, the architecture with the largest probability from the aforementioned distribution is selected, which is theoretically proved to be the optimal solution. The architectures searched by our method achieve remarkable top-1 accuracies with the magnitude times faster search process. Besides, RMI-NAS also generalizes to different datasets and search spaces. Our code has been made available at https://git.openi.org.cn/PCL_AutoML/XNAS.
Maintaining Reasoning Consistency in Compositional Visual Question Answering
Authors: Chenchen Jing, Yunde Jia, Yuwei Wu, Xinyu Liu, Qi Wu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

    A compositional question refers to a question that contains multiple visual concepts (e.g., objects, attributes, and relationships) and requires compositional reasoning to answer. Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions. For example, a compositional question for an image is: "Are there any elephants to the right of the white bird?" and one of its sub-questions is " Is any bird visible in the scene?". The models may answer "yes" to the compositional question, but "no" to the sub-question. This paper presents a dialog-like reasoning method for maintaining reasoning consistency in answering a compositional question and its sub-questions. Our method integrates the reasoning processes for the sub-questions into the reasoning process for the compositional question like a dialog task, and uses a consistency constraint to penalize inconsistent answer predictions. In order to enable quantitative evaluation of reasoning consistency, we construct a GQA-Sub dataset based on the well-organized GQA dataset. Experimental results on the GQA dataset and the GQA-Sub dataset demonstrate the effectiveness of our method.