pwc-archive/papers-with-abstracts huggingface dataset:
Calibrating Deep Neural Networks by Pairwise Constraints
Authors: Jiacheng Cheng, Nuno Vasconcelos
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Binary Classification

    It is well known that deep neural networks (DNNs) produce poorly calibrated estimates of class-posterior probabilities. We hypothesize that this is due to the limited calibration supervision provided by the cross-entropy loss, which places all emphasis on the probability of the true class and mostly ignores the remaining. We consider how each example can supervise all classes and show that the calibration of a C-way classification problem is equivalent to the calibration of C(C-1)/2 pairwise binary classification problems that can be derived from it. This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary problems. An implementation of this calibration by pairwise constraints (CPC) is then proposed, based on two types of binary calibration constraints. This is finally shown to be implementable with a very minimal increase in the complexity of cross-entropy training. Empirical evaluations of the proposed CPC method across multiple datasets and DNN architectures demonstrate state-of-the-art calibration performance.
FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback
Authors: Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, Pradeep Natarajan
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Retrieval, Retrieval

    Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously. We propose a new vision-language transformer based model, FashionVLP, that brings the prior knowledge contained in large image-text corpora to the domain of fashion image re-trieval, and combines visual information from multiple levels of context to effectively capture fashion related information. While queries are encoded through the transformer layers, our asymmetric design adopts a novel attention-based approach for fusing target image features without involving text or transformer layers in the process. Extensive results show that FashionVLP achieves the state-of-the-art performance on benchmark datasets, with a large 23% relative improvement on the challenging FashionIQ dataset, which contains complex natural language feedback.
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Authors: Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Object, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

    It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects. In this paper, we propose a novel Cross Language Image Matching (CLIMS) framework, based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress closely-related open background regions. In particular, we design object, background region and text label matching losses to guide the model to excite more reasonable object regions for CAM of each category. In addition, we design a co-occurring background suppression loss to prevent the model from activating closely-related background regions, with a predefined set of class-related background text descriptions. These designs enable the proposed CLIMS to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC2012 dataset show that our CLIMS significantly outperforms the previous state-of-the-art methods.
Negative-Aware Attention Framework for Image-Text Matching
Authors: Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image-text matching, Text Matching, text similarity

    Image-text matching, as a fundamental task, bridges the gap between vision and language. The key of this task is to accurately measure similarity between these two modalities. Prior work measuring this similarity mainly based on matched fragments (i.e., word/region with high relevance), while underestimating or even ignoring the effect of mismatched fragments (i.e., word/region with low relevance), e.g., via a typical LeaklyReLU or ReLU operation that forces negative scores close or exact to zero in attention. This work argues that mismatched textual fragments, which contain rich mismatching clues, are also crucial for image-text matching. We thereby propose a novel Negative-Aware Attention Framework (NAAF), which explicitly exploits both the positive effect of matched fragments and the negative effect of mismatched fragments to jointly infer image-text similarity. NAAF (1) delicately designs an iterative optimization method to maximally mine the mismatched fragments, facilitating more discriminative and robust negative effects, and (2) devises the two-branch matching mechanism to precisely calculate similarity/dissimilarity degrees for matched/mismatched fragments with different masks. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superior effectiveness of our NAAF, achieving state-of-the-art performance. Code will be released at: https://github.com/CrossmodalGroup/NAAF.
Investigating Top-k White-Box and Transferable Black-Box Attack
Authors: Chaoning Zhang, Philipp Benz, Adil Karjauv, Jae Won Cho, Kang Zhang, In So Kweon
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Existing works have identified the limitation of top-1 attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-box setting: transferable attack. It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength. Our work challenges this belief with empirical finding that stronger attack actually transfers better for the general top-k ASR indicated by the interest class rank (ICR) after attack. For increasing the attack strength, with an intuitive interpretation of the logit gradient from the geometric perspective, we identify that the weakness of the commonly used losses lie in prioritizing the speed to fool the network instead of maximizing its strength. To this end, we propose a new normalized CE loss that guides the logit to be updated in the direction of implicitly maximizing its rank distance from the ground-truth class. Extensive results in various settings have verified that our proposed new loss is simple yet effective for top-k attack. Code is available at: https://bit.ly/3uCiomP
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency
Authors: Jeany Son
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Contrastive Learning, Self-Supervised Learning

    We propose a novel probabilistic method employing Bayesian Model Averaging and self-cycle regularization for spatio-temporal correspondence learning in videos within a self-supervised learning framework. Most existing methods for self-supervised correspondence learning suffer from noisy labels that come with the data for free, and the presence of occlusion exacerbates the problem. We tackle this issue within a probabilistic framework that handles model uncertainty inherent in the path selection problem built on a complete graph. We propose a self-cycle regularization to consider a cycle-consistency property on individual edges in order to prevent converging on noisy matching or trivial solutions. We also utilize a mixture of sequential Bayesian filters to estimate posterior distribution for targets. In addition, we present a domain contrastive loss to learn discriminative representation among videos. Our algorithm is evaluated on various datasets for video label propagation tasks including DAVIS2017, VIP and JHMDB, and shows outstanding performances compared to the state-of-the-art self-supervised learning based video correspondence algorithms. Moreover, our method converges significantly faster than previous methods.
Exploring Effective Data for Surrogate Training Towards Black-Box Attack
Authors: Xuxiang Sun, Gong Cheng, Hongda Li, Lei Pei, Junwei Han
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Adversarial Attack, Diversity

    Without access to the training data where a black-box victim model is deployed, training a surrogate model for black-box adversarial attack is still a struggle. In terms of data, we mainly identify three key measures for effective surrogate training in this paper. First, we show that leveraging the loss introduced in this paper to enlarge the inter-class similarity makes more sense than enlarging the inter-class diversity like existing methods. Next, unlike the approaches that expand the intra-class diversity in an implicit model-agnostic fashion, we propose a loss function specific to the surrogate model for our generator to enhance the intra-class diversity. Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training. To this end, we propose a triple-player framework by introducing a discriminator into the traditional data-free framework. In this way, our method can be competitive when there are few semantic overlaps between the scarce proxy data (with the size between 1k and 5k) and the training data. We evaluate our method on a range of victim models and datasets. The extensive results witness the effectiveness of our method. Our source code is available at https://github.com/xuxiangsun/ST-Data.
SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
Authors: Zhi Chen, Kun Sun, Fan Yang, Wenbing Tao
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image to Point Cloud Registration, Point Cloud Registration

    In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2) measure to compute the similarity between correspondences. It considers the global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at early stage. Based on this measure, our registration pipeline employs a global spectral technique to find some reliable seeds from the initial correspondences. Then we design a two-stage strategy to expand each seed to a consensus set based on the SC^2 measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm to generate a candidate rigid transformation and select the best model as the final result. Our method can guarantee to find a certain number of outlier-free consensus sets using fewer samplings, making the model estimation more efficient and robust. In addition, the proposed SC^2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to investigate the performance of our method.
Weakly-Supervised Generation and Grounding of Visual Descriptions With Conditional Generative Models
Authors: Effrosyni Mavroudi, René Vidal
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Sentence

    Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input. Recent weakly-supervised approaches leverage region proposals and ground words based on the region attention coefficients of captioning models. To predict each next word in the sentence they attend over regions using a summary of the previous words as a query, and then ground the word by selecting the most attended regions. However, this leads to sub-optimal grounding, since attention coefficients are computed without taking into account the word that needs to be localized. To address this shortcoming, we propose a novel Grounded Visual Description Conditional Variational Autoencoder (GVD-CVAE) and leverage its latent variables for grounding. In particular, we introduce a discrete random variable that models each word-to-region alignment, and learn its approximate posterior distribution given the full sentence. Experiments on challenging image and video datasets (Flickr30k Entities, YouCook2, ActivityNet Entities) validate the effectiveness of our conditional generative model, showing that it can substantially outperform soft-attention-based baselines in grounding.
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC
Authors: Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Face Recognition

    Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets and margin-based softmax loss is the current state-of-the-art approach for face recognition. However, the memory and computing cost of the Fully Connected (FC) layer linearly scales up to the number of identities in the training set. Besides, the large-scale training data inevitably suffers from inter-class conflict and long-tailed distribution. In this paper, we propose a sparsely updating variant of the FC layer, named Partial FC (PFC). In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss. All class centers are still maintained throughout the whole training process, but only a subset is selected and updated in each iteration. Therefore, the computing requirement, the probability of inter-class conflict, and the frequency of passive update on tail class centers, are dramatically reduced. Extensive experiments across different training data and backbones (e.g. CNN and ViT) confirm the effectiveness, robustness and efficiency of the proposed PFC.
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation
Authors: Youngho Yoon, Inchul Chung, Lin Wang, Kuk-Jin Yoon
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: ERP, Image Super-Resolution, Super-Resolution

    The 360deg imaging has recently gained much attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured using a fisheye lens with the same sensor size. Therefore, it is beneficial to super-resolve a 360deg image. Several attempts have been made, but mostly considered equirectangular projection (ERP) as one of the ways for 360deg image representation despite the latitude-dependent distortions. In that case, as the output high-resolution (HR) image is always in the same ERP format as the lowresolution (LR) input, additional information loss may occur when transforming the HR image to other projection types. In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360deg image, with the goal of predicting the RGB values at given spherical coordinates for superresolution with an arbitrary 360deg image projection. Specifically, first we propose a feature extraction module that represents the spherical data based on an icosahedron and that efficiently extracts features on the spherical surface. We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates. As such, SphereSR flexibly reconstructs an HR image given an arbitrary projection type. Experiments on various benchmark datasets show that the proposed method significantly surpasses existing methods in terms of performance.
DeeCap: Dynamic Early Exiting for Efficient Image Captioning
Authors: Zhengcong Fei, Xu Yan, Shuhui Wang, Qi Tian
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Captioning, Imitation Learning

    Both accuracy and efficiency are crucial for image captioning in real-world scenarios. Although Transformer-based models have gained significant improved captioning performance, their computational cost is very high. A feasible way to reduce the time complexity is to exit the prediction early in internal decoding layers without passing the entire model. However, it is not straightforward to devise early exiting into image captioning due to the following issues. On one hand, the representation in shallow layers lacks high-level semantic and sufficient cross-modal fusion information for accurate prediction. On the other hand, the exiting decisions made by internal classifiers are unreliable sometimes. To solve these issues, we propose DeeCap framework for efficient image captioning, which dynamically selects proper-sized decoding layers from a global perspective to exit early. The key to successful early exiting lies in the specially designed imitation learning mechanism, which predicts the deep layer activation with shallow layer features. By deliberately merging the imitation learning into the whole image captioning architecture, the imitated deep layer representation can mitigate the loss brought by the missing of actual deep layers when early exiting is undertaken, resulting in significant reduction in calculation cost with small sacrifice of accuracy. Experiments on the MS COCO and Flickr30k datasets demonstrate the DeeCap can achieve competitive performances with 4 speed-up. Code is available at: https://github.com/feizc/DeeCap.
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation
Authors: Shengqu Cai, Anton Obukhov, Dengxin Dai, Luc van Gool
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D-Aware Image Synthesis, Image Generation, NeRF, Novel View Synthesis, Super-Resolution, Translation

    We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our method is based on pi-GAN, a generative model for unconditional 3D-aware image synthesis, which maps random latent codes to radiance fields of a class of objects. We jointly optimize (1) the pi-GAN objective to utilize its high-fidelity 3D-aware generation and (2) a carefully designed reconstruction objective. The latter includes an encoder coupled with pi-GAN generator to form an auto-encoder. Unlike previous few-shot NeRF approaches, our pipeline is unsupervised, capable of being trained with independent images without 3D, multi-view, or pose supervision. Applications of our pipeline include 3d avatar generation, object-centric novel view synthesis with a single input image, and 3d-aware super-resolution, to name a few.
Weakly Supervised Rotation-Invariant Aerial Object Detection Network
Authors: Xiaoxu Feng, Xiwen Yao, Gong Cheng, Junwei Han
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

    Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images. Existing predominant WSOD approaches built on regular CNNs which are not inherently designed to tackle object rotations without corresponding constraints, thereby leading to rotation-sensitive object detector. Meanwhile, current solutions have been prone to fall into the issue with unstable detectors, as they ignore lower-scored instances and may regard them as backgrounds. To address these issues, in this paper, we construct a novel end-to-end weakly supervised Rotation-Invariant aerial object detection Network (RINet). It is implemented with a flexible multi-branch online detector refinement, to be naturally more rotation-perceptive against oriented objects. Specifically, RINet first performs label propagating from the predicted instances to their rotated ones in a progressive refinement manner. Meanwhile, we propose to couple the predicted instance labels among different rotation-perceptive branches for generating rotation-consistent supervision and meanwhile pursuing all possible instances. With the rotation-consistent supervisions, RINet enforces and encourages consistent yet complementary feature learning for WSOD without additional annotations and hyper-parameters. On the challenging NWPU VHR-10.v2 and DIOR datasets, extensive experiments clearly demonstrate that we significantly boost existing WSOD methods to a new state-of-the-art performance. The code will be available at: https://github.com/XiaoxFeng/RINet.
SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage
Authors: Jiahao Yu, Li Chen, Mingrui Zhang, Mading Li
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Image collage task aims to create an informative and visual-aesthetic visual summarization for an image collection. While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree. Our key idea is to soften the discrete tree structure space into a continuous probability space. We propose SoftCollage, a novel method that employs a neural-based differentiable probabilistic tree generator to produce the probability distribution of correlation-preserving collage tree conditioned on deep image feature, aspect ratio and canvas size. The differentiable characteristic allows us to formulate the tree-based collage generation as a differentiable process and directly exploit gradient to optimize the collage layout in the level of probability space in an end-to-end manner. To facilitate image collage research, we propose AIC, a large-scale public-available annotated dataset for image collage evaluation. Extensive experiments on the introduced dataset demonstrate the superior performance of the proposed method. Data and codes are available at https://github.com/ChineseYjh/SoftCollage.