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Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tell Me What Happened: Unifying Text-guided Video Completion...

引用

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

作者： Fu, Tsu-Jui Yu, Licheng Zhang, Ning Fu, Cheng-Yang Su, Jong-Chyi Wang, William Yang Bell, Sean UC Santa Barbara Santa Barbara CA 93106 USA Meta Menlo Pk CA USA NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9798350301298

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC.

关键词： and reasoning language vision

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Visual Atoms: Pre-training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-training Vision Transformers with Sinusoid...

引用

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

作者： Takashima, Sora Hayamizu, Ryo Inoue, Nakamasa Kataoka, Hirokatsu Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350301298

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViTBase, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

关键词： Datasets and evaluation

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Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection

Bridging Precision and Confidence: A Train-Time Loss for Cal...

引用

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

作者： Munir, Muhammad Akhtar Khan, Muhammad Haris Khan, Salman Khan, Fahad Shahhaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Informat Technol Univ Lahore Pakistan Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350301298

Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that they tend to provide overconfident predictions and thus are poorly calibrated. The majority of the works addressing the miscalibration of DNNs fall under the scope of classification and consider only in-domain predictions. However, there is little to no progress in studying the calibration of DNN-based object detection models, which are central to many vision-based safety-critical applications. In this paper, inspired by the train-time calibration methods, we propose a novel auxiliary loss formulation that explicitly aims to align the class confidence of bounding boxes with the accurateness of predictions (i.e. precision). Since the original formulation of our loss depends on the counts of true positives and false positives in a mini-batch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions. We perform extensive experiments on challenging in-domain and out-domain scenarios with six benchmark datasets including MS-COCO, Cityscapes, Sim10k, and BDD100k. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios. Our source code and pre-trained models are available at https://***/akhtarvision/bpc_calibration

关键词： vision applications and systems

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Glocal Energy-based Learning for Few-Shot Open-Set recognition

Glocal Energy-based Learning for Few-Shot Open-Set Recogniti...

引用

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

作者： Wang, Haoyu Pang, Guansong Wang, Peng Zhang, Lei Wei, Wei Zhang, Yanning Northwestern Polytech Univ Xian Peoples R China Singapore Management Univ Singapore Singapore Univ Wollonong Wollongong NSW Australia

ISBN: (纸本)9798350301298

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.(1)

关键词： detection recognition: Categorization retrieval

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SuperDisco: Super-Class Discovery Improves Visual recognition for the Long-Tail

SuperDisco: Super-Class Discovery Improves Visual Recognitio...

引用

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

作者： Du, Yingjun Shen, Jiayi Zhen, Xiantong Snoek, Cees G. M. Univ Amsterdam AIM Lab Amsterdam Netherlands Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates United Imaging Healthcare Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350301298

Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the learned tail features more discriminative. This phenomenon motivated us to propose SuperDisco, an algorithm that discovers super-class representations for long-tailed recognition using a graph model. We learn to construct the super-class graph to guide the representation learning to deal with long-tailed distributions. Through message passing on the super-class graph, image representations are rectified and refined by attending to the most relevant entities based on the semantic similarity among their super-classes. Moreover, we propose to meta-learn the super-class graph under the supervision of a prototype graph constructed from a small amount of imbalanced data. By doing so, we obtain a more robust super-class graph that further improves the long-tailed recognition performance. The consistent state-of-the-art experiments on the long-tailed CIFAR-100, ImageNet, Places and iNaturalist demonstrate the benefit of the discovered super-class graph for dealing with long-tailed distributions.

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

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SVFormer: Semi-supervised Video Transformer for Action recognition

SVFormer: Semi-supervised Video Transformer for Action Recog...

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

作者： Xing, Zhen Dai, Qi Hu, Han Chen, Jingjing Wu, Zuxuan Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To this end, we introduce SVFormer, which adopts a steady pseudo-labeling framework (i.e., EMA-Teacher) to cope with unlabeled video samples. While a wide range of data augmentations have been shown effective for semi-supervised image classification, they generally produce limited results for video recognition. We therefore introduce a novel augmentation strategy, Tube Token-Mix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis. In addition, we propose a temporal warping augmentation to cover the complex temporal variation in videos, which stretches selected frames to various temporal durations in the clip. Extensive experiments on three datasets Kinetics-400, UCF-101, and HMDB-51 verify the advantage of SVFormer. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400. Our method can hopefully serve as a strong benchmark and encourage future search on semi-supervised action recognition with Transformer networks. Code is released at https://***/ChenHsing/SVFormer.

关键词： Video: Action and event understanding

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2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsu...

引用

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

作者： Kennerley, Mikhail Wang, Jian-Gang Veeravalli, Bharadwaj Tan, Robby T. Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9798350301298

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%, and to supervised models trained directly on the target data. (1)

关键词： detection recognition: Categorization retrieval

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DIP: Dual Incongruity Perceiving Network for Sarcasm Detection

DIP: Dual Incongruity Perceiving Network for Sarcasm Detecti...

引用

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

作者： Wen, Changsong Jia, Guoli Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image and text as demonstrated in psychological theories. To tackle this issue, we propose a Dual Incongruity Perceiving (DIP) network consisting of two branches to mine the sarcastic information from factual and affective levels. For the factual aspect, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings, and leverage gaussian distribution to model the uncertain correlation caused by the incongruity. The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data. For the affective aspect, we utilize siamese layers with shared parameters to learn cross-modal sentiment information. Furthermore, we use the polarity value to construct a relation graph for the mini-batch, which forms the continuous contrastive loss to acquire affective embeddings. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches. Our code is released on https://***/downdric/MSD.

关键词： vision applications and systems

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Internal Diverse Image Completion

Internal Diverse Image Completion

引用

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

作者： Alkobi, Noa Shaham, Tamar Rott Michaeli, Tomer Technion Haifa Israel MIT Technion Haifa Israel

ISBN: (纸本)9798350302493

Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.

关键词： computer vision

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Fake it till you make it: Learning transferable representations from synthetic ImageNet clones

Fake it till you make it: Learning transferable representati...

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

作者： Sariyildiz, Mert Bulent Alahari, Karteek Larlus, Diane Kalantidis, Yannis NAVER LABS Europe Grenoble France Uni Grenoble Alpes CNRS Inria Grenoble INPLJK Grenoble France

ISBN: (纸本)9798350301298

Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://***/imagenet-sd

关键词： detection recognition: Categorization retrieval

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