mtybilly/Instruct2DS huggingface dataset:
Few-Shot Semantic Image Synthesis With Class Affinity Transfer
Authors: Jakob Verbeek, Stéphane Lathuilière Marlène Careil
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of images annotated with per-pixel label maps that are extremely tedious to obtain. To alleviate the high annotation cost, we propose a transfer method that leverages a model trained on a large source dataset to improve the learning ability on small target datasets via estimated pairwise relations between source and target classes. The class affinity matrix is introduced as a first layer to the source model to make it compatible with the target label maps, and the source model is then further fine-tuned for the target domain. To estimate the class affinities we consider different approaches to leverage prior knowledge: semantic segmentation on the source domain, textual label embeddings, and self-supervised vision features. We apply our approach to GAN-based and diffusion-based architectures for semantic synthesis. Our experiments show that the different ways to estimate class affinity can effectively combined, and that our approach significantly improves over existing state-of-the-art transfer approaches for generative image models.
Implicit Identity Driven Deepfake Face Swapping Detection
Authors: Zhongyuan Wang, Jifan Yang, Jiaxin Ai, Qin Zou, Qian Wang, Dengpan Ye Baojin Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit identity and implicit identity, which respectively corresponds to the identity of the source face and target face during face swapping. Note that the explicit identities of faces can be extracted by regular face recognizers. Particularly, the implicit identity of real face is consistent with the its explicit identity. Thus the difference between explicit and implicit identity of face facilitates face swapping detection. Following this idea, we propose a novel implicit identity driven framework for face swapping detection. Specifically, we design an explicit identity contrast (EIC) loss and an implicit identity exploration (IIE) loss, which supervises a CNN backbone to embed face images into the implicit identity space. Under the guidance of EIC, real samples are pulled closer to their explicit identities, while fake samples are pushed away from their explicit identities. Moreover, IIE is derived from the margin-based classification loss function, which encourages the fake faces with known target identities to enjoy intra-class compactness and inter-class diversity. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art counterparts.
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation
Authors: Zilei Wang, Weinan He Yixin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: This work focuses on a practical knowledge transfer task defined as Source-Free Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and unlabeled target data are available. To fully utilize source knowledge, we propose to transfer the class relationship, which is domain-invariant but still under-explored in previous works. To this end, we first regard the classifier weights of the source model as class prototypes to compute class relationship, and then propose a novel probability-based similarity between target-domain samples by embedding the source-domain class relationship, resulting in Class Relationship embedded Similarity (CRS). Here the inter-class term is particularly considered in order to more accurately represent the similarity between two samples, in which the source prior of class relationship is utilized by weighting. Finally, we propose to embed CRS into contrastive learning in a unified form. Here both class-aware and instance discrimination contrastive losses are employed, which are complementary to each other. We combine the proposed method with existing representative methods to evaluate its efficacy in multiple SFUDA settings. Extensive experimental results reveal that our method can achieve state-of-the-art performance due to the transfer of domain-invariant class relationship.
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
Authors: Grace Bezold, Aman Bhatta, Kevin W. Bowyer Haiyu Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme and applied it to create a new facial hair attribute dataset, FH37K. Face attribute research also so far has not dealt with logical consistency and completeness. For example, in prior research, an image might be classified as both having no beard and also having a goatee (a type of beard). We show that the test accuracy of previous classification methods on facial hair attribute classification drops significantly if logical consistency of classifications is enforced. We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes. Using an attribute classifier trained on FH37K, we investigate how facial hair affects face recognition accuracy, including variation across demographics. Results show that similarity and difference in facial hairstyle have important effects on the impostor and genuine score distributions in face recognition. The code is at https:// github.com/ HaiyuWu/ facial hair logical.
One-to-Few Label Assignment for End-to-End Dense Detection
Authors: Minghan Li, Ruihuang Li, Chenhang He, Lei Zhang Shuai Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for lightweight end-to-end dense detection. However, o2o can largely degrade the feature learning performance due to the limited number of positive samples. Though extra positive samples can be introduced to mitigate this issue, the computation of self- and cross- attentions among anchors prevents its practical application to dense and fully convolutional detectors. In this work, we propose a simple yet effective one-to-few (o2f) label assignment strategy for end-to-end dense detection. Apart from defining one positive and many negative anchors for each object, we define several soft anchors, which serve as positive and negative samples simultaneously. The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to 'representation learning' in the early training stage and contribute more to 'duplicated prediction removal' in the later stage. The detector trained in this way can not only learn a strong feature representation but also perform end-to-end detection. Experiments on COCO and CrowdHuman datasets demonstrate the effectiveness of the proposed o2f scheme.
Spatio-Temporal Pixel-Level Contrastive Learning-Based Source-Free Domain Adaptation for Video Semantic Segmentation
Authors: Poojan Oza, Sumanth Chennupati, Alejandro Galindo, Vishal M. Patel Shao-Yuan Lo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data. However, the access to source data is often restricted or infeasible in real-world scenarios. Under the source data restrictive circumstances, UDA is less practical. To address this, recent works have explored solutions under the Source-Free Domain Adaptation (SFDA) setup, which aims to adapt a source-trained model to the target domain without accessing source data. Still, existing SFDA approaches use only image-level information for adaptation, making them sub-optimal in video applications. This paper studies SFDA for Video Semantic Segmentation (VSS), where temporal information is leveraged to address video adaptation. Specifically, we propose Spatio-Temporal Pixel-Level (STPL) contrastive learning, a novel method that takes full advantage of spatio-temporal information to tackle the absence of source data better. STPL explicitly learns semantic correlations among pixels in the spatio-temporal space, providing strong self-supervision for adaptation to the unlabeled target domain. Extensive experiments show that STPL achieves state-of-the-art performance on VSS benchmarks compared to current UDA and SFDA approaches. Code is available at: https://github.com/shaoyuanlo/STPL
InternImage: Exploring Large-Scale Vision Foundation Models With Deformable Convolutions
Authors: Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, Xiaogang Wang, Yu Qiao Wenhai Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.
DAA: A Delta Age AdaIN Operation for Age Estimation via Binary Code Transformer
Authors: Xingpeng Zhang, Ye Li, Ju Tao, Bin Xiao, Bing Wang, Zongjie Jiang Ping Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation. We let the input of transfer learning as the binary code of age natural number to obtain continuous age feature information. The learned two groups of values in Binary code mapping are corresponding to the mean and standard deviation of the comparison ages. In summary, our method consists of four parts: FaceEncoder, DAA operation, Binary code mapping, and AgeDecoder modules. After getting the delta age via AgeDecoder, we take the average value of all comparison ages and delta ages as the predicted age. Compared with state-of-the-art methods, our method achieves better performance with fewer parameters on multiple facial age datasets. Code is available at https://github.com/redcping/Delta_Age_AdaIN
Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones
Authors: Karteek Alahari, Diane Larlus, Yannis Kalantidis Mert Bülent Sarıyıldız
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: 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://europe.naverlabs.com/imagenet-sd
Mind the Label Shift of Augmentation-Based Graph OOD Generalization
Authors: Jian Liang, Ran He Junchi Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Out-of-distribution (OOD) generalization is an important issue for Graph Neural Networks (GNNs). Recent works employ different graph editions to generate augmented environments and learn an invariant GNN for generalization. However, the graph structural edition inevitably alters the graph label. This causes the label shift in augmentations and brings inconsistent predictive relationships among augmented environments. To address this issue, we propose LiSA, which generates label-invariant augmentations to facilitate graph OOD generalization. Instead of resorting to graph editions, LiSA exploits Label-invariant Subgraphs of the training graphs to construct Augmented environments. Specifically, LiSA first designs the variational subgraph generators to efficiently extract locally predictive patterns and construct multiple label-invariant subgraphs. Then, the subgraphs produced by different generators are collected to build different augmented environments. To promote diversity among augmented environments, LiSA further introduces a tractable energy-based regularization to enlarge pair-wise distances between the distributions of environments. In this manner, LiSA generates diverse augmented environments with a consistent predictive relationship to facilitate learning an invariant GNN. Extensive experiments on node-level and graph-level OOD benchmarks show that LiSA achieves impressive generalization performance with different GNN backbones. Code is available on https://github.com/Samyu0304/LiSA.
Unsupervised Intrinsic Image Decomposition With LiDAR Intensity
Authors: Yasuhiro Yao, Taiga Yoshida, Takuhiro Kaneko, Shingo Ando, Jun Shimamura Shogo Sato
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization
Authors: Boseung Jeong, Suha Kwak Sungyeon Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieves this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since the geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER is evaluated on four standard benchmarks, where it consistently improved the performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.
Diffusion Probabilistic Model Made Slim
Authors: Daquan Zhou, Jiashi Feng, Xinchao Wang Xingyi Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Despite the visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerating the testing yet overlooked their huge complexity and size. In this paper, we make a dedicated attempt to lighten DPM while striving to preserve its favourable performance. We start by training a small-sized latent diffusion model (LDM) from scratch but observe a significant fidelity drop in the synthetic images. Through a thorough assessment, we find that DPM is intrinsically biased against high-frequency generation, and learns to recover different frequency components at different time-steps. These properties make compact networks unable to represent frequency dynamics with accurate high-frequency estimation. Towards this end, we introduce a customized design for slim DPM, which we term as Spectral Diffusion (SD), for lightweight image synthesis. SD incorporates wavelet gating in its architecture to enable frequency dynamic feature extraction at every reverse steps, and conducts spectrum-aware distillation to promote high-frequency recovery by inverse weighting the objective based on spectrum magnitudes. Experimental results demonstrate that, SD achieves 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks while retaining competitive image fidelity.
Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
Authors: Xingchen Ma, Matthew B. Blaschko Junyi Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via locally adapted models. In this work, we present a novel framework for PFL based on hierarchical Bayesian modeling and variational inference. A global model is introduced as a latent variable to augment the joint distribution of clients' parameters and capture the common trends of different clients, optimization is derived based on the principle of maximizing the marginal likelihood and conducted using variational expectation maximization. Our algorithm gives rise to a closed-form estimation of a confidence value which comprises the uncertainty of clients' parameters and local model deviations from the global model. The confidence value is used to weigh clients' parameters in the aggregation stage and adjust the regularization effect of the global model. We evaluate our method through extensive empirical studies on multiple datasets. Experimental results show that our approach obtains competitive results under mild heterogeneous circumstances while significantly outperforming state-of-the-art PFL frameworks in highly heterogeneous settings.
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
Authors: Chenqiang Gao, Fangcen Liu, Pengcheng Li, Deyu Meng, Xinbo Gao Chuandong Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudo-labeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSDA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://github.com/azhuantou/HSSDA.
Interactive and Explainable Region-Guided Radiology Report Generation
Authors: Philip Müller, Georgios Kaissis, Daniel Rueckert Tim Tanida
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg.
MED-VT: Multiscale Encoder-Decoder Video Transformer With Application To Object Segmentation
Authors: He Zhao, Richard P. Wildes, Mennatullah Siam Rezaul Karim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Multiscale video transformers have been explored in a wide variety of vision tasks. To date, however, the multiscale processing has been confined to the encoder or decoder alone. We present a unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in videos. Multiscale representation at both encoder and decoder yields key benefits of implicit extraction of spatiotemporal features (i.e. without reliance on input optical flow) as well as temporal consistency at encoding and coarse-to-fine detection for high-level (e.g. object) semantics to guide precise localization at decoding. Moreover, we propose a transductive learning scheme through many-to-many label propagation to provide temporally consistent predictions.We showcase our Multiscale Encoder-Decoder Video Transformer (MED-VT) on Automatic Video Object Segmentation (AVOS) and actor/action segmentation, where we outperform state-of-the-art approaches on multiple benchmarks using only raw images, without using optical flow.
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces
Authors: Ivan Skorokhodov, Peter Wonka Yiqun Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: A signed distance function (SDF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three new components. The first component is to borrow the tri-plane representation from EG3D and represent signed distance fields as a mixture of tri-planes and MLPs instead of representing it with MLPs only. Using tri-planes leads to a more expressive data structure but will also introduce noise in the reconstructed surface. The second component is to use a new type of positional encoding with learnable weights to combat noise in the reconstruction process. We divide the features in the tri-plane into multiple frequency scales and modulate them with sin and cos functions of different frequencies. The third component is to use learnable convolution operations on the tri-plane features using self-attention convolution to produce features with different frequency bands. The experiments show that PET-NeuS achieves high-fidelity surface reconstruction on standard datasets. Following previous work and using the Chamfer metric as the most important way to measure surface reconstruction quality, we are able to improve upon the NeuS baseline by 57% on Nerf-synthetic (0.84 compared to 1.97) and by 15.5% on DTU (0.71 compared to 0.84). The qualitative evaluation reveals how our method can better control the interference of high-frequency noise.
ZegCLIP: Towards Adapting CLIP for Zero-Shot Semantic Segmentation
Authors: Yinjie Lei, Bowen Zhang, Lingqiao Liu, Yifan Liu Ziqin Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a wo-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed the cropped proposal regions to CLIP to utilize its image-level zero-shot classification capability. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. In this work, we pursue a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level. Our investigation starts with a straightforward extension as our baseline that generates semantic masks by comparing the similarity between text and patch embeddings extracted from CLIP. However, such a paradigm could heavily overfit the seen classes and fail to generalize to unseen classes. To handle this issue, we propose three simple-but-effective designs and figure out that they can significantly retain the inherent zero-shot capacity of CLIP and improve pixel-level generalization ability. Incorporating those modifications leads to an efficient zero-shot semantic segmentation system called ZegCLIP. Through extensive experiments on three public benchmarks, ZegCLIP demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both "inductive" and "transductive" zero-shot settings. In addition, compared with the two-stage method, our one-stage ZegCLIP achieves a speedup of about 5 times faster during inference. We release the code at https://github.com/ZiqinZhou66/ZegCLIP.git.
AdaptiveMix: Improving GAN Training via Feature Space Shrinkage
Authors: Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng Haozhe Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Due to the outstanding capability for data generation, Generative Adversarial Networks (GANs) have attracted considerable attention in unsupervised learning. However, training GANs is difficult, since the training distribution is dynamic for the discriminator, leading to unstable image representation. In this paper, we address the problem of training GANs from a novel perspective, i.e., robust image classification. Motivated by studies on robust image representation, we propose a simple yet effective module, namely AdaptiveMix, for GANs, which shrinks the regions of training data in the image representation space of the discriminator. Considering it is intractable to directly bound feature space, we propose to construct hard samples and narrow down the feature distance between hard and easy samples. The hard samples are constructed by mixing a pair of training images. We evaluate the effectiveness of our AdaptiveMix with widely-used and state-of-the-art GAN architectures. The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples. We also show that our AdaptiveMix can be further applied to image classification and Out-Of-Distribution (OOD) detection tasks, by equipping it with state-of-the-art methods. Extensive experiments on seven publicly available datasets show that our method effectively boosts the performance of baselines. The code is publicly available at https://github.com/WentianZhang-ML/AdaptiveMix.
Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models To Learn Any Unseen Style
Authors: Hazarapet Tunanyan, Kai Wang, Shant Navasardyan, Zhangyang Wang, Humphrey Shi Haoming Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Diffusion models have demonstrated impressive capability of text-conditioned image synthesis, and broader application horizons are emerging by personalizing those pretrained diffusion models toward generating some specialized target object or style. In this paper, we aim to learn an unseen style by simply fine-tuning a pre-trained diffusion model with a handful of images (e.g., less than 10), so that the fine-tuned model can generate high-quality images of arbitrary objects in this style. Such extremely lowshot fine-tuning is accomplished by a novel toolkit of finetuning techniques, including text-to-image customized data augmentations, a content loss to facilitate content-style disentanglement, and sparse updating that focuses on only a few time steps. Our framework, dubbed Specialist Diffusion, is plug-and-play to existing diffusion model backbones and other personalization techniques. We demonstrate it to outperform the latest few-shot personalization alternatives of diffusion models such as Textual Inversion and DreamBooth, in terms of learning highly sophisticated styles with ultra-sample-efficient tuning. We further show that Specialist Diffusion can be integrated on top of textual inversion to boost performance further, even on highly unusual styles. Our codes are available at: https://github.com/Picsart-AI-Research/Specialist-Diffusion
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Authors: Heon Song, Seonwook Park, Donggeun Yoo, Sérgio Pereira Mingu Kang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings.
Planning-Oriented Autonomous Driving
Authors: Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, Hongyang Li Yihan Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative errors or deficient task coordination. Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query interfaces to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven by substantially outperforming previous state-of-the-arts in all aspects. Code and models are public.
HyperCUT: Video Sequence From a Single Blurry Image Using Unsupervised Ordering
Authors: Phong Tran, Anh Tran, Cuong Pham, Rang Nguyen, Minh Hoai Bang-Dang Pham
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: 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://github.com/VinAIResearch/HyperCUT.git
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
Authors: Xinlei He, Ning Yu, Michael Backes, Yang Zhang Zeyang Sha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Self-supervised representation learning techniques have been developing rapidly to make full use of unlabeled images. They encode images into rich features that are oblivious to downstream tasks. Behind their revolutionary representation power, the requirements for dedicated model designs and a massive amount of computation resources expose image encoders to the risks of potential model stealing attacks - a cheap way to mimic the well-trained encoder performance while circumventing the demanding requirements. Yet conventional attacks only target supervised classifiers given their predicted labels and/or posteriors, which leaves the vulnerability of unsupervised encoders unexplored. In this paper, we first instantiate the conventional stealing attacks against encoders and demonstrate their severer vulnerability compared with downstream classifiers. To better leverage the rich representation of encoders, we further propose Cont-Steal, a contrastive-learning-based attack, and validate its improved stealing effectiveness in various experiment settings. As a takeaway, we appeal to our community's attention to the intellectual property protection of representation learning techniques, especially to the defenses against encoder stealing attacks like ours.
Document Image Shadow Removal Guided by Color-Aware Background
Authors: Yinghao He, Qing Zhang, Zheng Liu, Xiaolong Zhang, Chunxia Xiao Ling Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document. Furthermore, we propose a background-guided document images shadow removal network (BGShadowNet) using the predicted spatially varying background as auxiliary information, which consists of two stages. At Stage I, a background-constrained decoder is designed to promote a coarse result. Then, the coarse result is refined with a background-based attention module (BAModule) to maintain a consistent appearance and a detail improvement module (DEModule) to enhance the texture details at Stage II. Experiments on two benchmark datasets qualitatively and quantitatively validate the superiority of the proposed approach over state-of-the-arts.
Independent Component Alignment for Multi-Task Learning
Authors: Nikolay Patakin, Arseny Kuznetsov, Anton Konushin Dmitry Senushkin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. Despite rapid progress in the field, MTL remains challenging due to optimization issues such as conflicting and dominating gradients. In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization. We theoretically demonstrate that a condition number reflects the aforementioned optimization issues. Accordingly, we present Aligned-MTL, a novel MTL optimization approach based on the proposed criterion, that eliminates instability in the training process by aligning the orthogonal components of the linear system of gradients. While many recent MTL approaches guarantee convergence to a minimum, task trade-offs cannot be specified in advance. In contrast, Aligned-MTL provably converges to an optimal point with pre-defined task-specific weights, which provides more control over the optimization result. Through experiments, we show that the proposed approach consistently improves performance on a diverse set of MTL benchmarks, including semantic and instance segmentation, depth estimation, surface normal estimation, and reinforcement learning.
Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision
Authors: Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy Aditay Tripathi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViTB) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration
Authors: Tal Remez, Bowen Shi, Jacob Donley, Yossi Adi Wei-Ning Hsu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Regeneration, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech while not necessarily preserving the rest such as voice. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual regeneration tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github.io/ReVISE.
Improved Distribution Matching for Dataset Condensation
Authors: Guanbin Li, Yipeng Qin, Yizhou Yu Ganlong Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Dataset Condensation aims to condense a large dataset into a smaller one while maintaining its ability to train a well-performing model, thus reducing the storage cost and training effort in deep learning applications. However, conventional dataset condensation methods are optimization-oriented and condense the dataset by performing gradient or parameter matching during model optimization, which is computationally intensive even on small datasets and models. In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising. Specifically, we identify two important shortcomings of naive distribution matching (i.e., imbalanced feature numbers and unvalidated embeddings for distance computation) and address them with three novel techniques (i.e., partitioning and expansion augmentation, efficient and enriched model sampling, and class-aware distribution regularization). Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources, thereby scaling data condensation to larger datasets and models. Extensive experiments demonstrate the effectiveness of our method. Codes are available at https://github.com/uitrbn/IDM