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Is BERT Blind? Exploring the Effect of vision-and-Language Pretraining on Visual Language Understanding

Is BERT Blind? Exploring the Effect of Vision-and-Language P...

引用

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

作者： Alper, Morris Fiman, Michael Averbuch-Elor, Hadar Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modelling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being under-performed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts.

关键词： and reasoning language vision

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DegAE: A New Pretraining Paradigm for Low-level vision

DegAE: A New Pretraining Paradigm for Low-level Vision

引用

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

作者： Liu, Yihao He, Jingwen Gu, Jinjin Kong, Xiangtao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for highcost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词： Low-level vision

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Soft Augmentation for Image Classification

Soft Augmentation for Image Classification

引用

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

作者： Liu, Yang Yan, Shen Leal-Taixe, Laura Hays, James Ramanan, Deva Argo AI Pittsburgh PA 15222 USA

ISBN: (纸本)9798350301298

Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample. We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e.g., more aggressive image crop augmentations produce less confident learning targets. We demonstrate that soft targets allow for more aggressive data augmentation, offer more robust performance boosts, work with other augmentation policies, and interestingly, produce better calibrated models (since they are trained to be less confident on aggressively cropped/occluded examples). Combined with existing aggressive augmentation strategies, soft targets 1) double the top-1 accuracy boost across Cifar-10, Cifar-100, ImageNet-1K, and ImageNet-V2, 2) improve model occlusion performance by up to 4x, and 3) half the expected calibration error (ECE). Finally, we show that soft augmentation generalizes to self-supervised classification tasks. Code available at https://***/youngleox/soft_augmentation

关键词： detection recognition: Categorization retrieval

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UMat: Uncertainty-Aware Single Image High Resolution Material Capture

UMat: Uncertainty-Aware Single Image High Resolution Materia...

引用

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

作者： Rodriguez-Pardo, Carlos Dominguez-Elvira, Henar Pascual-Hernandez, David Garces, Elena SEDDI Madrid Spain Univ Rey Juan Carlos Madrid Spain

ISBN: (纸本)9798350301298

We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be illposed -more than a single diffuse image might be needed to disambiguate the specular reflection- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment.

关键词： vision + graphics

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Recurrent vision Transformers for Object Detection with Event Cameras

Recurrent Vision Transformers for Object Detection with Even...

引用

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

作者： Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

We present Recurrent vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: first, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (< 12 ms on a T4 GPU) and favorable parameter efficiency (5x fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. Code: https://***/uzh-rpg/RVT

关键词： detection recognition: Categorization retrieval

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Structured Sparsity Learning for Efficient Video Super-Resolution

Structured Sparsity Learning for Efficient Video Super-Resol...

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

作者： Xia, Bin He, Jingwen Zhang, Yulun Wang, Yitong Tian, Yapeng Yang, Wenming Van Gool, Luc Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Swiss Fed Inst Technol Zurich Switzerland ByteDance Inc Beijing Peoples R China Univ Texas Dallas Dallas TX USA

ISBN: (纸本)9798350301298

The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, e.g., smartphones and drones. Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. The code is available at https://***/Zj-BinXia/SSL.

关键词： Low-level vision

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HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering

HyperCUT: Video Sequence from a Single Blurry Image using Un...

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

作者： Bang-Dang Pham Phong Tran Anh Tran Cuong Pham Rang Nguyen Minh Hoai VinAI Res Hanoi Vietnam MBZUAI Abu Dhabi U Arab Emirates Posts & Telecommun Inst Tech Ho Chi Minh City Vietnam SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://***/VinAIResearch/ ***

关键词： Low-level vision

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Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

Dynamic Conceptional Contrastive Learning for Generalized Ca...

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

作者： Pu, Nan Zhong, Zhun Sebe, Nicu Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9798350301298

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL) framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https: //***/TPCD/DCCL

关键词： vision applications and systems

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Improving Table Structure recognition with Visual-Alignment Sequential Coordinate Modeling

Improving Table Structure Recognition with Visual-Alignment ...

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

作者： Huang, Yongshuai Lu, Ning Chen, Dapeng Li, Yibo Xie, Zecheng Zhu, Shenggao Gao, Liangcai Peng, Wei Huawei Technol Ltd Shenzhen Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, the previous methods struggle with imprecise bounding boxes as the logical representation lacks local visual information. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.

关键词： Document analysis and understanding

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DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation

DualVector: Unsupervised Vector Font Synthesis with Dual-Par...

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

作者： Liu, Ying-Tian Zhang, Zhifei Guo, Yuan-Chen Fisher, Matthew Wang, Zhaowen Zhang, Song-Hai Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization. On the other hand, existing vector font synthesis methods either fail to represent the shape concisely or require vector supervision during training. To push the quality of vector font synthesis to the next level, we propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs. The glyph contour is then obtained by boolean operations on these paths. We first learn such a representation only from glyph images and devise a subsequent contour refinement step to align the contour with an image representation to further enhance details. Our method, named DualVector, outperforms state-of-the-art methods in vector font synthesis both quantitatively and qualitatively. Our synthesized vector fonts can be easily converted to common digital font formats like TrueType Font for practical use. The code is released at https://***/thuliu-yt16/dualvector.

关键词： vision + graphics

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