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Seeing What You Miss: vision-Language Pre-training with Semantic Completion Learning

Seeing What You Miss: Vision-Language Pre-training with Sema...

引用

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

作者： Ji, Yatai Tu, Rongcheng Jiang, Jie Kong, Weijie Cai, Chengfei Zhao, Wenzhe Wang, Hongfa Yang, Yujiu Liu, Wei Tsinghua Univ Beijing Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

关键词： Multi-modal learning

来源：评论

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Difficulty-based Sampling for Debiased Contrastive Representation Learning

Difficulty-based Sampling for Debiased Contrastive Represent...

引用

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

作者： Jang, Taeuk Wang, Xiaoqian Purdue Univ 465 Northwestern Ave W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

Contrastive learning is a self-supervised representation learning method that achieves milestone performance in various classification tasks. However, due to its unsupervised fashion, it suffers from the false negative sample problem: randomly drawn negative samples that are assumed to have a different label but actually have the same label as the anchor. This deteriorates the performance of contrastive learning as it contradicts the motivation of contrasting semantically similar and dissimilar pairs. This raised the attention and the importance of finding legitimate negative samples, which should be addressed by distinguishing between 1) true vs. false negatives;2) easy vs. hard negatives. However, previous works were limited to the statistical approach to handle false negative and hard negative samples with hyperparameters tuning. In this paper, we go beyond the statistical approach and explore the connection between hard negative samples and data bias. We introduce a novel debiased contrastive learning method to explore hard negatives by relative difficulty referencing the bias amplifying counterpart. We propose triplet loss for training a biased encoder that focuses more on easy negative samples. We theoretically show that the triplet loss amplifies the bias in self-supervised representation learning. Finally, we empirically show the proposed method improves downstream classification performance.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Vis...

引用

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

作者： Wu, Yanmin Cheng, Xinhua Zhang, Renrui Cheng, Zesen Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, Scan-Refer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://***/yanmin-wu/EDA.

关键词： language reasoning vision

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

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

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi Jilin Univ Sch Artificial Intelligence Jilin Peoples R China Zhejiang Univ Hangzhou Peoples R China Wayne State Univ Dept Comp Sci Wayne NJ USA MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China

ISBN: (纸本)9798350301298

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

关键词： vision + graphics

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A Large-scale Robustness Analysis of Video Action recognition Models

A Large-scale Robustness Analysis of Video Action Recognitio...

引用

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

作者： Schiappa, Madeline Chantry Biyani, Naman Kamtam, Prudvi Vyas, Shruti Palangi, Hamid Vineet, Vibhav Rawat, Yogesh Univ Cent Florida CRCV Orlando FL 32816 USA IIT Kanpur Kanpur Uttar Pradesh India Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We focus on robustness against real-world distribution shift perturbations instead of adversarial perturbations. We propose four different benchmark datasets, HMDB51-P, UCF101-P, Kinetics400-P, and SSv2-P to perform this analysis. We study robustness of six state-of-the-art action recognition models against 90 different perturbations. The study reveals some interesting findings, 1) transformer based models are consistently more robust compared to CNN based models, 2) Pretraining improves robustness for Transformer based models more than CNN based models, and 3) All of the studied models are robust to temporal perturbations for all datasets but SSv2;suggesting the importance of temporal information for action recognition varies based on the dataset and activities. Next, we study the role of augmentations in model robustness and present a real-world dataset, UCF101-DS, which contains realistic distribution shifts, to further validate some of these findings. We believe this study will serve as a benchmark for future research in robust video action recognition

关键词： Datasets and evaluation

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Interpretable Object recognition by Semantic Prototype Analysis

Interpretable Object Recognition by Semantic Prototype Analy...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wan, Qiyang Wang, Ruiping Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a recognition model with such principles to explain the recognition process to enhance human trust. We propose Semantic Prototype Analysis Network (SPANet), an interpretable object recognition approach that enables models to explicate the decision process more lucidly and comprehensibly to humans by "pointing out where to focus" and "telling about why it is" simultaneously. With the proposed method, some part prototypes with semantic concepts will be provided to elaborate on the classification together with a group of visualized samples to achieve both part-wise and semantic interpretability. The results of extensive experiments demonstrate that SPANet is able to recognize objects almost as well as the non-interpretable models, at the same time generating intelligible explanations for its decision process.

关键词： accountable Algorithms Algorithms ethical computer vision Explainable fair Image recognition and understanding privacy-preserving

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Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Event-based Video Frame Interpolation with Cross-Modal Asymm...

引用

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

作者： Kim, Taewoo Chae, Yujeong Jang, Hyun-Kurl Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350301298

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://***/intelpro/CBMNet

关键词： Low-level vision

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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...

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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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Correspondence Transformers with Asymmetric Feature Learning and Matching Flow Super-Resolution

Correspondence Transformers with Asymmetric Feature Learning...

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

作者： Sun, Yixuan Zhao, Dongyang Yin, Zhangyue Huang, Yiwen Gui, Tao Zhang, Wenqiang Ge, Weifeng Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9798350301298

This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://***/YXSUNMADMAX/ACTR.

关键词： detection recognition: Categorization retrieval

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Improved Test-Time Adaptation for Domain Generalization

Improved Test-Time Adaptation for Domain Generalization

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

作者： Chen, Liang Zhang, Yong Song, Yibing Shan, Ying Liu, Lingqiao Univ Adelaide Adelaide SA Australia Tencent AI Lab Shenzhen Peoples R China Fudan Univ AI3 Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time training (TTT), which adapts the learned model with test data, might be a promising solution to the problem. Generally, a TTT strategy hinges its performance on two main factors: selecting an appropriate auxiliary TTT task for updating and identifying reliable parameters to update during the test phase. Both previous arts and our experiments indicate that TTT may not improve but be detrimental to the learned model if those two factors are not properly considered. This work addresses those two factors by proposing an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically defining an auxiliary objective, we propose a learnable consistency loss for the TTT task, which contains learnable parameters that can be adjusted toward better alignment between our TTT task and the main prediction task. Second, we introduce additional adaptive parameters for the trained model, and we suggest only updating the adaptive parameters during the test phase. Through extensive experiments, we show that the proposed two strategies are beneficial for the learned model (see Figure 1), and ITTA could achieve superior performance to the current state-of-the-art methods on several DG benchmarks. Code is available at https://***/liangchen527/ITTA.

关键词： detection recognition: Categorization retrieval

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