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Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning Deep Latent Variable Models by Short-Run MCMC Infer...

引用

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

作者： An, Dongsheng Xie, Jianwen Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781665445092

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a short-run MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.

关键词： Training Monte Carlo methods Costs Image synthesis Markov processes pattern recognition Task analysis

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Efficient RGB-T Tracking via Cross-Modality Distillation

Efficient RGB-T Tracking via Cross-Modality Distillation

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tianlu Zhang Hongyuan Guo Qiang Jiao Qiang Zhang Jungong Han School of Mechano-Electronic Engineering Xidian University China Department of Computer Science the University of Sheffield UK Centre for Machine Intelligence the University of Sheffield UK

Most current RGB-T trackers adopt a two-stream structure to extract unimodal RGB and thermal features and complex fusion strategies to achieve multi-modal feature fusion, which require a huge number of parameters, thus hindering their real-life applications. On the other hand, a compact RGB-T tracker may be computationally efficient but encounter non-negligible performance degradation, due to the weakening of feature representation ability. To remedy this situation, a cross-modality distillation framework is presented to bridge the performance gap between a compact tracker and a powerful tracker. Specifically, a specific-common feature distillation module is proposed to transform the modality-common information as well as the modality-specific information from a deeper two-stream network to a shallower single-stream network. In addition, a multi-path selection distillation module is proposed to instruct a simple fusion module to learn more accurate multi-modal information from a well-designed fusion mechanism by using multiple paths. We validate the effectiveness of our method with extensive experiments on three RGB-T benchmarks, which achieves state-of-the-art performance but consumes much less computational resources.

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ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zhida Feng Zhenyu Zhang Xintong Yu Yewei Fang Lanxin Li Xuyi Chen Yuxiang Lu Jiaxiang Liu Weichong Yin Shikun Feng Yu Sun Li Chen Hao Tian Hua Wu Haifeng Wang Baidu Inc. School of Computer Science and Technology Wuhan University of Science and Technology

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 1 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.

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Behavior-Driven Synthesis of Human Dynamics

Behavior-Driven Synthesis of Human Dynamics

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

作者： Blattmann, Andreas Milbich, Timo Dorkenwald, Michael Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany

ISBN: (纸本)9781665445092

Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively.

关键词： computer vision Video sequences Process control pattern recognition Task analysis

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Visual recognition-Driven Image Restoration for Multiple Degradation with Intrinsic Semantics Recovery

Visual Recognition-Driven Image Restoration for Multiple Deg...

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conference on computer vision and pattern recognition (cvpr)

作者： Zizheng Yang Jie Huang Jiahao Chang Man Zhou Hu Yu Jinghao Zhang Feng Zhao University of Science and Technology of China

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

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Adversarial Robustness Across Representation Spaces

Adversarial Robustness Across Representation Spaces

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

作者： Awasthi, Pranjal Yu, George Ferng, Chun-Sung Tomkins, Andrew Juan, Da-Cheng Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Adversarial robustness corresponds to the susceptibility of deep neural networks to imperceptible perturbations made at test time. In the context of image tasks, many algorithms have been proposed to make neural networks robust to adversarial perturbations made to the input pixels. These perturbations are typically measured in an 4, norm. However, robustness often holds only for the specific attack used for training. In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representations spaces. For the case of image data, examples include the standard pixel representation as well as the representation in the discrete cosine transform (DCT) basis. We design a theoretically sound algorithm with formal guarantees for the above problem. Furthermore, our guarantees also hold when the goal is to require robustness with respect to multiple 4, norm based attacks. We then derive an efficient practical implementation and demonstrate the effectiveness of our approach on standard datasets for image classification.(1)

关键词： Training Deep learning computer vision Perturbation methods Neural networks Robustness pattern recognition

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Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering

Discovering the Real Association: Multimodal Causal Reasonin...

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conference on computer vision and pattern recognition (cvpr)

作者： Chuanqi Zang Hanqing Wang Mingtao Pei Wei Liang School of Computer Science and Technology Beijing Institute of Technology Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing

Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on statistical evidence causes, which ignores potential bias in the multimodal data. In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. For visual data, question-irrelevant objects may establish simple matching associations with the answer. For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences. Therefore, to enhance the generalization of the model, we discover the real association by explicitly capturing visual features that are causally related to the question semantics and weakening the impact of local language semantics on question answering. The experimental results on two large causal VideoQA datasets verify that our proposed framework 1) improves the accuracy of the existing VideoQA backbone, 2) demonstrates robustness on complex scenes and questions. The code will be released at https://***/Chuanqi-Zang/Discovering-the-Real-Association.

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BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View recognition via Perspective Supervision

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-...

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conference on computer vision and pattern recognition (cvpr)

作者： Chenyu Yang Yuntao Chen Hao Tian Chenxin Tao Xizhou Zhu Zhaoxiang Zhang Gao Huang Hongyang Li Yu Qiao Lewei Lu Jie Zhou Jifeng Dai Tsinghua University Centre for Artificial Intelligence and Robotics HKISI_CAS Sense Time Research Institute of Automation Chinese Academy of Science (CASIA) Shanghai Artificial Intelligence Laboratory

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and bet-suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pretrained backbones like Vo Vn et, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector; where proposals from the perspective head are fed into the bird’ s-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the gener-ality of the proposed detector. The proposed method is ver-ified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.

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S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S3C: Semi-Supervised VQA Natural Language Explanation via Se...

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conference on computer vision and pattern recognition (cvpr)

作者： Wei Suo Mengyang Sun Weisong Liu Yiqi Gao Peng Wang Yanning Zhang Qi Wu School of Computer Science and Ningbo Institute Northwestern Polytechnical University China School of Cybersecurity Northwestern Polytechnical University China University of Adelaide Australia

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or selfrationalization models to obtain a plausible explanation. However, these frameworks are bottle-necked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S 3 C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the S 3 C can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

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Randomized Adversarial Training via Taylor Expansion

Randomized Adversarial Training via Taylor Expansion

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conference on computer vision and pattern recognition (cvpr)

作者： Gaojie Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang State Key Laboratory of Computer Science Institute of Software CAS Beijing China University of Liverpool Liverpool UK Lancaster University Lancaster UK

In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the most successful methods. To deal with both the robustness against adversarial examples and the accuracy over clean examples, many works develop enhanced adversarial training methods to achieve various trade-offs between them [[19], [38], [80]], Leveraging over the studies [8], [32] that smoothed update on weights during training may help find flat minima and improve generalization, we suggest reconciling the robustness-accuracy trade-off from another perspective, i.e., by adding random noise into deterministic weights. The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima. With PGD, CW, and Auto Attacks, an extensive set of experiments demonstrate that our method enhances the state-of-the-art adversarial training methods, boosting both robustness and clean accuracy. The code is available at https://***/Alexkael/Randomized-Adversarial-Training.

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