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The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training

The Dialog Must Go On: Improving Visual Dialog via Generativ...

引用

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

作者： Kang, Gi-Cheon Kim, Sungdong Kim, Jin-Hwa Kwak, Donghyun Zhang, Byoung-Tak Seoul Natl Univ IPAI Seoul South Korea AIIS Seoul South Korea NAVER AI Lab Seongnam South Korea NAVER Cloud CLOVA Seongnam South Korea

ISBN: (纸本)9798350301298

Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or leveraged pre-training on related vision-and-language datasets. This paper presents a semi-supervised learning approach for visually-grounded dialog, called Generative Self-Training (GST), to leverage unlabeled images on the Web. Specifically, GST first retrieves in-domain images through out-of-distribution detection and generates synthetic dialogs regarding the images via multimodal conditional text generation. GST then trains a dialog agent on the synthetic and the original VisDial data. As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1.2M. 12.9M QA data). For robust training of the synthetic dialogs, we also propose perplexity-based data selection and multimodal consistency regularization. Evaluation on VisDial v1.0 and v0.9 datasets shows that GST achieves new state-of-the-art results on both datasets. We further observe the robustness of GST against both visual and textual adversarial attacks. Finally, GST yields strong performance gains in the low-data regime. Code is available at https: //***/gicheonkang/gst-visdial.

关键词： and reasoning language vision

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Re-thinking Model Inversion Attacks Against Deep Neural Networks

Re-thinking Model Inversion Attacks Against Deep Neural Netw...

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

作者： Nguyen, Ngoc-Bao Chandrasegaran, Keshigeyan Abdollahzadeh, Milad Cheung, Ngai-Man SUTD Singapore Singapore

ISBN: (纸本)9798350301298

Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive information (e.g. private face images used in training a face recognition system). Recently, several algorithms for MI have been proposed to improve the attack performance. In this work, we revisit MI, study two fundamental issues pertaining to all state-of-the-art (SOTA) MI algorithms, and propose solutions to these issues which lead to a significant boost in attack performance for all SOTA MI. In particular, our contributions are two-fold: 1) We analyze the optimization objective of SOTA MI algorithms, argue that the objective is sub-optimal for achieving MI, and propose an improved optimization objective that boosts attack performance significantly. 2) We analyze "MI overfitting", show that it would prevent reconstructed images from learning semantics of training data, and propose a novel "model augmentation" idea to overcome this issue. Our proposed solutions are simple and improve all SOTA MI attack accuracy significantly. E.g., in the standard CelebA benchmark, our solutions improve accuracy by 11.8% and achieve for the first time over 90% attack accuracy. Our findings demonstrate that there is a clear risk of leaking sensitive information from deep learning models. We urge serious consideration to be given to the privacy implications. Our code, demo, and models are available at https:// ***/rethinking_model_inversion_attacks/.

关键词： accountability ethics in vision fairness privacy Transparency

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Balanced Product of Calibrated Experts for Long-Tailed recognition

Balanced Product of Calibrated Experts for Long-Tailed Recog...

引用

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

作者： Aimar, Emanuel Sanchez Jonnarth, Arvi Felsberg, Michael Kuhlmann, Marco Linkoping Univ Dept Elect Engn Linkoping Sweden Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Husqvarna Grp Huskvarna Sweden Univ KwaZulu Natal Durban South Africa

ISBN: (纸本)9798350301298

Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://***/emasa/BalPoE-CalibratedLT.

关键词： continual low-shot meta or long-tail learning Transfer

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OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

OReX: Object Reconstruction from Planar Cross-sections Using...

引用

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

作者： Sawdayee, Haim Vaxman, Amir Bermano, Amit H. Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9798350301298

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.

关键词： Physics-based vision shape-from-X

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Detecting Everything in the Open World: Towards Universal Object Detection

Detecting Everything in the Open World: Towards Universal Ob...

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

作者： Wang, Zhenyu Li, Yali Chen, Xi Lim, Ser-Nam Torralba, Antonio Zhao, Hengshuang Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Meta AI New York NY USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has the ability to recognize enormous categories in the open world. The critical points for the universality of UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces for training through the alignment of image and text spaces, which guarantees sufficient information for universal representations. 2) it generalizes to the open world easily while keeping the balance between seen and unseen classes, thanks to abundant information from both vision and language modalities. 3) it further promotes the generalization ability to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7k categories, the largest measurable category size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot generalization ability on large-vocabulary datasets - it surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images. On 13 public detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data. (1)

关键词： detection recognition: Categorization retrieval

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Meta-Explore: Exploratory Hierarchical vision-and-Language Navigation Using Scene Object Spectrum Grounding

Meta-Explore: Exploratory Hierarchical Vision-and-Language N...

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

作者： Hwang, Minyoung Jeong, Jaeyeon Kim, Minsoo Oh, Yoonseon Oh, Songhwai Seoul Natl Univ Elect & Comp Engn Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Hanyang Univ Dept Elect Engn Seoul South Korea Seoul Natl Univ Interdisciplinary Major Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350301298

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rateby17.1% andSPLby20.6% against the state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/***

关键词： Robotics

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Bit-shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization

Bit-shrinking: Limiting Instantaneous Sharpness for Improvin...

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

作者： Lin, Chen Peng, Bo Li, Zheyang Tan, Wenming Ren, Ye Xiao, Jun Pu, Shiliang Hikvis Res Inst Hangzhou Peoples R China Zhe Jiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is an effective compression method to reduce the model size and computational cost. However, quantizing a model into a low-bit one, e.g., lower than 4, is difficult and often results in non-negligible performance degradation. To address this, we investigate the loss landscapes of quantized networks with various bit-widths. We show that the network with more ragged loss surface, is more easily trapped into bad local minima, which mostly appears in low-bit quantization. A deeper analysis indicates, the ragged surface is caused by the injection of excessive quantization noise. To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise. To smooth the rugged loss surface, we propose to limit the sharpness term small and stable during optimization. Instead of directly optimizing the target bit network, we design a self-adapted shrinking scheduler for the bit-width in continuous domain from high bit-width to the target by limiting the increasing sharpness term within a proper range. It can be viewed as iteratively adding small "instant" quantization noise and adjusting the network to eliminate its impact. Widely experiments including classification and detection tasks demonstrate the effectiveness of the Bit-shrinking strategy in PTQ. On the vision Transformer models, our INT8 and INT6 models drop within 0.5% and 1.5% Top-1 accuracy, respectively. On the traditional CNN networks, our INT4 quantized models drop within 1.3% and 3.5% Top-1 accuracy on ResNet18 and MobileNetV2 without fine-tuning, which achieves the state-of-the-art performance.

关键词： Efficient and scalable vision

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Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior

Uncertainty-Aware Unsupervised Image Deblurring with Deep Re...

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

作者： Tang, Xiaole Zhao, Xile Liu, Jun Wang, Jianli Miao, Yuchun Zeng, Tieyong Univ Elect Sci & Technol China Chengdu Peoples R China Northeast Normal Univ Changchun Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the clear image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to model-driven and data-driven methods in terms of image quality and the robustness to different types of kernel error.

关键词： Explainable computer vision

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Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

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

作者： Liang, Feng Wu, Bichen Dai, Xiaoliang Li, Kunpeng Zhao, Yinan Zhang, Hang Zhang, Peizhao Vajda, Peter Marculescu, Diana Univ Texas Austin Austin TX 78712 USA Meta Real Labs Burlingame CA USA Cruise Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the "blank" areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset specific adaptations.

关键词： and reasoning language vision

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MAGVLT: Masked Generative vision-and-Language Transformer

MAGVLT: Masked Generative Vision-and-Language Transformer

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

作者： Kim, Sungwoong Jo, Daejin Lee, Donghoon Kim, Jongmin Korea Univ Dept Artificial Intelligence Seoul South Korea Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350301298

While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unified generative vision-and-language (VL) model that can produce both images and text sequences. Especially, we propose a generative VL transformer based on the non-autoregressive mask prediction, named MAGVLT, and compare it with an autoregressive generative VL transformer (ARGVLT). In comparison to ARGVLT, the proposed MAGVLT enables bidirectional context encoding, fast decoding by parallel token predictions in an iterative refinement, and extended editing capabilities such as image and text infilling. For rigorous training of our MAGVLT with image-text pairs from scratch, we combine the image-to-text, text-to-image, and joint image-and-text mask prediction tasks. Moreover, we devise two additional tasks based on the step-unrolled mask prediction and the selective prediction on the mixture of two image-text pairs. Experimental results on various downstream generation tasks of VL benchmarks show that our MAGVLT outperforms ARGVLT by a large margin even with significant inference speedup. Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderatesized model (fewer than 500M parameters) even without the use of monomodal data and networks.

关键词： Multi-modal learning

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