mtybilly/Instruct2DS huggingface dataset:
Few-Shot Geometry-Aware Keypoint Localization
Authors: Gaurav Bharaj, David Ferman, Helge Rhodin, Pablo Garrido Xingzhe He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint localization with fewer yet consistently annotated images. To this end, we present a novel formulation that learns to localize semantically consistent keypoint definitions, even for occluded regions, for varying object categories. We use a few user-labeled 2D images as input examples, which are extended via self-supervision using a larger unlabeled dataset. Unlike unsupervised methods, the few-shot images act as semantic shape constraints for object localization. Furthermore, we introduce 3D geometry-aware constraints to uplift keypoints, achieving more accurate 2D localization. Our general-purpose formulation paves the way for semantically conditioned generative modeling and attains competitive or state-of-the-art accuracy on several datasets, including human faces, eyes, animals, cars, and never-before-seen mouth interior (teeth) localization tasks, not attempted by the previous few-shot methods. Project page: https://xingzhehe.github.io/FewShot3DKP/
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Authors: Zexiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, Paul Guerrero Titas Anciukevičius
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision. Central to our method is a novel image denoising architecture that generates and renders an intermediate three-dimensional representation of a scene in each denoising step. This enforces a strong inductive structure within the diffusion process, providing a 3D consistent representation while only requiring 2D supervision. The resulting 3D representation can be rendered from any view. We evaluate RenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive performance for generation of 3D scenes and inference of 3D scenes from 2D images. Additionally, our diffusion-based approach allows us to use 2D inpainting to edit 3D scenes.
Adaptive Data-Free Quantization
Authors: Yang Wang, Richang Hong, Meng Wang Biao Qian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions -- how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players -- a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaDFQ.
Neural Vector Fields: Implicit Representation by Explicit Learning
Authors: Guosheng Lin, Zhenghao Chen, Luping Zhou Xianghui Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3D representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UDFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as Vector Fields. Rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https://github.com/Wi-sc/NVF.
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Authors: Elad Richardson, Or Patashnik, Raja Giryes, Daniel Cohen-Or Gal Metzer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D object. We adapt the score distillation to the publicly available, and computationally efficient, Latent Diffusion Models, which apply the entire diffusion process in a compact latent space of a pretrained autoencoder. As NeRFs operate in image space, a naive solution for guiding them with latent score distillation would require encoding to the latent space at each guidance step. Instead, we propose to bring the NeRF to the latent space, resulting in a Latent-NeRF. Analyzing our Latent-NeRF, we show that while Text-to-3D models can generate impressive results, they are inherently unconstrained and may lack the ability to guide or enforce a specific 3D structure. To assist and direct the 3D generation, we propose to guide our Latent-NeRF using a Sketch-Shape: an abstract geometry that defines the coarse structure of the desired object. Then, we present means to integrate such a constraint directly into a Latent-NeRF. This unique combination of text and shape guidance allows for increased control over the generation process. We also show that latent score distillation can be successfully applied directly on 3D meshes. This allows for generating high-quality textures on a given geometry. Our experiments validate the power of our different forms of guidance and the efficiency of using latent rendering.
Learning Generative Structure Prior for Blind Text Image Super-Resolution
Authors: Wangmeng Zuo, Chen Change Loy Xiaoming Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a  codebook . The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://github.com/csxmli2016/MARCONet.
Overcoming the Trade-Off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
Authors: Chen Li, Linlin Yang, Xiaoxu Zheng, Michael Bi Mi, Gim Hee Lee, Angela Yao Ziwei Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Direct mesh fitting for 3D hand shape reconstruction estimates highly accurate meshes. However, the resulting meshes are prone to artifacts and do not appear as plausible hand shapes. Conversely, parametric models like MANO ensure plausible hand shapes but are not as accurate as the non-parametric methods. In this work, we introduce a novel weakly-supervised hand shape estimation framework that integrates non-parametric mesh fitting with MANO models in an end-to-end fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to yield well-aligned and high-quality 3D meshes, especially in challenging two-hand and hand-object interaction scenarios.
Open-Vocabulary Attribute Detection
Authors: Sudhanshu Mittal, Simon Ging, Thomas Brox María A. Bravo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models.
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training
Authors: Yutong Xie, Zilin Lu, Yong Xia Qingjie Zeng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://github.com/maxwell0027/PEFAT.
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Authors: Zi Wang, Yiqi Zhong, Junhao Ge, Siheng Chen Shaoheng Fang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods.
Ground-Truth Free Meta-Learning for Deep Compressive Sampling
Authors: Yuhui Quan, Tongyao Pang, Hui Ji Xinran Qin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Deep learning has become an important tool for reconstructing images in compressive sampling (CS). This paper proposes a ground-truth (GT) free meta-learning method for CS, which leverages both external and internal learning for unsupervised high-quality image reconstruction. The proposed method first trains a deep model via external meta-learning using only CS measurements, and then efficiently adapts the trained model to a test sample for further improvement by exploiting its internal characteristics. The meta-learning and model adaptation are built on an improved Stein's unbiased risk estimator (iSURE) that provides efficient computation and effective guidance for accurate prediction in the range space of the adjoint of the measurement matrix. To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time. Experimental results have demonstrated that the proposed GT-free method performs well, and can even compete with supervised learning-based methods.
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds
Authors: Huifang Feng, Kanle Shi, Yue Gao, Yi Fang, Yu-Shen Liu, Zhizhong Han Qing Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our SHS-Net outperforms the state-of-the-art methods in both unoriented and oriented normal estimation on the widely used benchmarks. The code, data and pretrained models are available at https://github.com/LeoQLi/SHS-Net.
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
Authors: Hong Cai, Risheek Garrepalli, Fatih Porikli Jisoo Jeong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixing ratio, we combine one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects and scenes. We refer to such pairs as distracted pairs. Our intuition is that using semantically meaningful distractors enables the model to learn related variations and attain robustness against challenging deviations, compared to conventional augmentation schemes focusing only on low-level aspects and modifications. More specifically, in addition to the supervised loss computed between the estimated flow for the original pair and its ground-truth flow, we include a second supervised loss defined between the distracted pair's flow and the original pair's ground-truth flow, weighted with the same mixing ratio. Furthermore, when unlabeled data is available, we extend our augmentation approach to self-supervised settings through pseudo-labeling and cross-consistency regularization. Given an original pair and its distracted version, we enforce the estimated flow on the distracted pair to agree with the flow of the original pair. Our approach allows increasing the number of available training pairs significantly without requiring additional annotations. It is agnostic to the model architecture and can be applied to training any optical flow estimation models. Our extensive evaluations on multiple benchmarks, including Sintel, KITTI, and SlowFlow, show that DistractFlow improves existing models consistently, outperforming the latest state of the art.
Test of Time: Instilling Video-Language Models With a Sense of Time
Authors: Makarand Tapaswi, Cees G. M. Snoek Piyush Bagad
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.
Learning To Segment Every Referring Object Point by Point
Authors: Yu Wu, Yunchao Wei, Wu Liu, Xiaodan Liang, Yao Zhao Mengxue Qu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., 1%) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves 52.06% in terms of accuracy (versus 58.93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations.
Seeing With Sound: Long-range Acoustic Beamforming for Multimodal Scene Understanding
Authors: Jim Aldon D’Souza, Ethan Tseng, Joe Bartusek, Felix Heide Praneeth Chakravarthula
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Existing autonomous vehicles primarily use sensors that rely on electromagnetic waves which are undisturbed in good environmental conditions but can suffer in adverse scenarios, such as low light or for objects with low reflectance. Moreover, only objects in direct line-of-sight are typically detected by these existing methods. Acoustic pressure waves emanating from road users do not share these limitations. However, such signals are typically ignored in automotive perception because they suffer from low spatial resolution and lack directional information. In this work, we introduce long-range acoustic beamforming of pressure waves from noise directly produced by automotive vehicles in-the-wild as a  complementary sensing modality  to traditional optical sensor approaches for detection of objects in dynamic traffic environments. To this end, we introduce the first multimodal long-range acoustic beamforming dataset. We propose a neural aperture expansion method for beamforming and we validate its utility for multimodal automotive object detection. We validate the benefit of adding sound detections to existing RGB cameras in challenging automotive scenarios, where camera-only approaches fail or do not deliver the ultra-fast rates of pressure sensors.
OpenScene: 3D Scene Understanding With Open Vocabularies
Authors: Kyle Genova, Chiyu “Max” Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser Songyou Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a model predicts dense features for 3D scene points that are co-embedded with text and image pixels in CLIP feature space. This zero-shot approach enables task-agnostic training and open-vocabulary queries. For example, to perform SOTA zero-shot 3D semantic segmentation it first infers CLIP features for every 3D point and later classifies them based on similarities to embeddings of arbitrary class labels. More interestingly, it enables a suite of open-vocabulary scene understanding applications that have never been done before. For example, it allows a user to enter an arbitrary text query and then see a heat map indicating which parts of a scene match. Our approach is effective at identifying objects, materials, affordances, activities, and room types in complex 3D scenes, all using a single model trained without any labeled 3D data.
Movies2Scenes: Using Movie Metadata To Learn Scene Representation
Authors: Chun-Hao Liu, Xiang Hao, Xiaohan Nie, Maxim Arap, Raffay Hamid Shixing Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Understanding scenes in movies is crucial for a variety of applications such as video moderation, search, and recommendation. However, labeling individual scenes is a time-consuming process. In contrast, movie level metadata (e.g., genre, synopsis, etc.) regularly gets produced as part of the film production process, and is therefore significantly more commonly available. In this work, we propose a novel contrastive learning approach that uses movie metadata to learn a general-purpose scene representation. Specifically, we use movie metadata to define a measure of movie similarity, and use it during contrastive learning to limit our search for positive scene-pairs to only the movies that are considered similar to each other. Our learned scene representation consistently outperforms existing state-of-the-art methods on a diverse set of tasks evaluated using multiple benchmark datasets. Notably, our learned representation offers an average improvement of 7.9% on the seven classification tasks and 9.7% improvement on the two regression tasks in LVU dataset. Furthermore, using a newly collected movie dataset, we present comparative results of our scene representation on a set of video moderation tasks to demonstrate its generalizability on previously less explored tasks.
Think Twice Before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
Authors: Penghao Wu, Li Chen, Jiangwei Xie, Conghui He, Junchi Yan, Hongyang Li Xiaosong Jia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: End-to-end autonomous driving has made impressive progress in recent years. Existing methods usually adopt the decoupled encoder-decoder paradigm, where the encoder extracts hidden features from raw sensor data, and the decoder outputs the ego-vehicle's future trajectories or actions. Under such a paradigm, the encoder does not have access to the intended behavior of the ego agent, leaving the burden of finding out safety-critical regions from the massive receptive field and inferring about future situations to the decoder. Even worse, the decoder is usually composed of several simple multi-layer perceptrons (MLP) or GRUs while the encoder is delicately designed (e.g., a combination of heavy ResNets or Transformer). Such an imbalanced resource-task division hampers the learning process. In this work, we aim to alleviate the aforementioned problem by two principles: (1) fully utilizing the capacity of the encoder; (2) increasing the capacity of the decoder. Concretely, we first predict a coarse-grained future position and action based on the encoder features. Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly. We also retrieve the encoder features around the predicted coordinate to obtain fine-grained information about the safety-critical region. Finally, based on the predicted future and the retrieved salient feature, we refine the coarse-grained position and action by predicting its offset from ground-truth. The above refinement module could be stacked in a cascaded fashion, which extends the capacity of the decoder with spatial-temporal prior knowledge about the conditioned future. We conduct experiments on the CARLA simulator and achieve state-of-the-art performance in closed-loop benchmarks. Extensive ablation studies demonstrate the effectiveness of each proposed module. Code and models are available at https://github.com/opendrivelab/ThinkTwice.
DSVT: Dynamic Sparse Voxel Transformer With Rotated Sets
Authors: Chen Shi, Shaoshuai Shi, Meng Lei, Sen Wang, Di He, Bernt Schiele, Liwei Wang Haiyang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is a fundamental problem in 3D perception. Compared with the customized sparse convolution, the attention mechanism in Transformers is more appropriate for flexibly modeling long-range relationships and is easier to be deployed in real-world applications. However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points. In this paper, we present Dynamic Sparse Voxel Transformer (DSVT), a single-stride window-based voxel Transformer backbone for outdoor 3D perception. In order to efficiently process sparse points in parallel, we propose Dynamic Sparse Window Attention, which partitions a series of local regions in each window according to its sparsity and then computes the features of all regions in a fully parallel manner. To allow the cross-set connection, we design a rotated set partitioning strategy that alternates between two partitioning configurations in consecutive self-attention layers. To support effective downsampling and better encode geometric information, we also propose an attention-style 3D pooling module on sparse points, which is powerful and deployment-friendly without utilizing any customized CUDA operations. Our model achieves state-of-the-art performance with a broad range of 3D perception tasks. More importantly, DSVT can be easily deployed by TensorRT with real-time inference speed (27Hz). Code will be available at https://github.com/Haiyang-W/DSVT.
Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
Authors: Tianzhu Ye, Shen Zhang, Yao Tang, Jiajun Liang Siyuan Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead to significant information loss. Our quantitative experiments reveal that the impact of pruned tokens on performance should be noticeable. To address this issue, we propose a novel joint Token Pruning & Squeezing module (TPS) for compressing vision transformers with higher efficiency. Firstly, TPS adopts pruning to get the reserved and pruned subsets. Secondly, TPS squeezes the information of pruned tokens into partial reserved tokens via the unidirectional nearest-neighbor matching and similarity-oriented fusing steps. Compared to state-of-the-art methods, our approach outperforms them under all token pruning intensities. Especially while shrinking DeiT-tiny&small computational budgets to 35%, it improves the accuracy by 1%-6% compared with baselines on ImageNet classification. The proposed method can accelerate the throughput of DeiT-small beyond DeiT-tiny, while its accuracy surpasses DeiT-tiny by 4.78%. Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy. Code is available at https://github.com/megvii-research/TPS-CVPR2023.
Enhancing the Self-Universality for Transferable Targeted Attacks
Authors: Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang Zhipeng Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this paper, we propose a novel transfer-based targeted attack method that optimizes the adversarial perturbations without any extra training efforts for auxiliary networks on training data. Our new attack method is proposed based on the observation that highly universal adversarial perturbations tend to be more transferable for targeted attacks. Therefore, we propose to make the perturbation to be agnostic to different local regions within one image, which we called as self-universality. Instead of optimizing the perturbations on different images, optimizing on different regions to achieve self-universality can get rid of using extra data. Specifically, we introduce a feature similarity loss that encourages the learned perturbations to be universal by maximizing the feature similarity between adversarial perturbed global images and randomly cropped local regions. With the feature similarity loss, our method makes the features from adversarial perturbations to be more dominant than that of benign images, hence improving targeted transferability. We name the proposed attack method as Self-Universality (SU) attack. Extensive experiments demonstrate that SU can achieve high success rates for transfer-based targeted attacks. On ImageNet-compatible dataset, SU yields an improvement of 12% compared with existing state-of-the-art methods. Code is available at https://github.com/zhipeng-wei/Self-Universality.
Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation With Cross-Scale Distortion Awareness
Authors: Zishuo Zheng, Chunyu Lin, Lang Nie, Kang Liao, Shuai Zheng, Yao Zhao Zhijie Shen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Based on the Manhattan World assumption, most existing indoor layout estimation schemes focus on recovering layouts from vertically compressed 1D sequences. However, the compression procedure confuses the semantics of different planes, yielding inferior performance with ambiguous interpretability. To address this issue, we propose to disentangle this 1D representation by pre-segmenting orthogonal (vertical and horizontal) planes from a complex scene, explicitly capturing the geometric cues for indoor layout estimation. Considering the symmetry between the floor boundary and ceiling boundary, we also design a soft-flipping fusion strategy to assist the pre-segmentation. Besides, we present a feature assembling mechanism to effectively integrate shallow and deep features with distortion distribution awareness. To compensate for the potential errors in pre-segmentation, we further leverage triple attention to reconstruct the disentangled sequences for better performance. Experiments on four popular benchmarks demonstrate our superiority over existing SoTA solutions, especially on the 3DIoU metric. The code is available at https://github.com/zhijieshen-bjtu/DOPNet.
EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points
Authors: Wenbin Lin, Feng Xu Chengwei Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and even support topological changes. Input with an image sequence from a single camera, our network is trained fully automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art. Our code and captured data are available at https://chengwei-zheng.github.io/EditableNeRF/.
Neural Map Prior for Autonomous Driving
Authors: Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao Xuan Xiong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: High-definition (HD) semantic maps are a crucial component for autonomous driving on urban streets. Traditional offline HD maps are created through labor-intensive manual annotation processes, which are costly and do not accommodate timely updates. Recently, researchers have proposed to infer local maps based on online sensor observations. However, the range of online map inference is constrained by sensor perception range and is easily affected by occlusions. In this work, we propose Neural Map Prior (NMP), a neural representation of global maps that enables automatic global map updates and enhances local map inference performance. To incorporate the strong map prior into local map inference, we leverage cross-attention to dynamically capture the correlations between current features and prior features. For updating the global neural map prior, we use a learning-based fusion module to guide the network in fusing features from previous traversals. This design allows the network to capture a global neural map prior while making sequential online map predictions. Experimental results on the nuScenes dataset demonstrate that our framework is compatible with most map segmentation/detection methods, improving map prediction performance in challenging weather conditions and over an extended horizon. To the best of our knowledge, this represents the first learning-based system for constructing a global map prior.
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
Authors: Huixia Li, Xiawu Zheng, Xuefeng Xiao, Rui Wang, Shilei Wen, Xin Pan, Fei Chao, Rongrong Ji Yuexiao Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to solve this problem by introducing a principled and generalized framework theoretically. In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity. To this end, we define the module capacity (ModCap) under data-dependent and data-free scenarios, where the differentials between adjacent modules are used to measure the degree of oscillation. The problem is then solved by selecting top-k differentials, in which the corresponding modules are jointly optimized and quantized. Extensive experiments demonstrate that our method successfully reduces the performance drop and is generalized to different neural networks and PTQ methods. For example, with 2/4 bit ResNet-50 quantization, our method surpasses the previous state-of-the-art method by 1.9%. It becomes more significant on small model quantization, e.g. surpasses BRECQ method by 6.61% on MobileNetV2*0.5.
PEAL: Prior-Embedded Explicit Attention Learning for Low-Overlap Point Cloud Registration
Authors: Luwei Ren, Yu Zhang, Wenhui Zhou, Lili Lin, Guojun Dai Junle Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Learning distinctive point-wise features is critical for low-overlap point cloud registration. Recently, it has achieved huge success in incorporating Transformer into point cloud feature representation, which usually adopts a self-attention module to learn intra-point-cloud features first, then utilizes a cross-attention module to perform feature exchange between input point clouds. Self-attention is computed by capturing the global dependency in geometric space. However, this global dependency can be ambiguous and lacks distinctiveness, especially in indoor low-overlap scenarios, as which the dependence with an extensive range of non-overlapping points introduces ambiguity. To address this issue, we present PEAL, a Prior-embedded Explicit Attention Learning model. By incorporating prior knowledge into the learning process, the points are divided into two parts. One includes points lying in the putative overlapping region and the other includes points lying in the putative non-overlapping region. Then PEAL explicitly learns one-way attention with the putative overlapping points. This simplistic design attains surprising performance, significantly relieving the aforementioned feature ambiguity. Our method improves the Registration Recall by 6+% on the challenging 3DLoMatch benchmark and achieves state-of-the-art performance on Feature Matching Recall, Inlier Ratio, and Registration Recall on both 3DMatch and 3DLoMatch. Code will be made publicly available.
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
Authors: Jipeng Lyu, Yu-Xiong Wang Jun-Kun Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or "plots" the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks. Notably, NeuralEditor supports both zero-shot inference and further fine-tuning over the edited scene. Our code, benchmark, and demo video are available at https://immortalco.github.io/NeuralEditor.
NIKI: Neural Inverse Kinematics With Invertible Neural Networks for 3D Human Pose and Shape Estimation
Authors: Siyuan Bian, Qi Liu, Jiasheng Tang, Fan Wang, Cewu Lu Jiefeng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://github.com/Jeff-sjtu/NIKI
Masked Image Modeling With Local Multi-Scale Reconstruction
Authors: Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhi-Hong Deng, Kai Han Haoqing Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Unfortunately, MIM models typically have huge computational burden and slow learning process, which is an inevitable obstacle for their industrial applications. Although the lower layers play the key role in MIM, existing MIM models conduct reconstruction task only at the top layer of encoder. The lower layers are not explicitly guided and the interaction among their patches is only used for calculating new activations. Considering the reconstruction task requires non-trivial inter-patch interactions to reason target signals, we apply it to multiple local layers including lower and upper layers. Further, since the multiple layers expect to learn the information of different scales, we design local multi-scale reconstruction, where the lower and upper layers reconstruct fine-scale and coarse-scale supervision signals respectively. This design not only accelerates the representation learning process by explicitly guiding multiple layers, but also facilitates multi-scale semantical understanding to the input. Extensive experiments show that with significantly less pre-training burden, our model achieves comparable or better performance on classification, detection and segmentation tasks than existing MIM models.