mtybilly/Instruct2DS huggingface dataset:
An End-to-End Transformer Model for 3D Object Detection
Authors: Rohit Girdhar, Armand Joulin Ishan Misra
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block. Specifically, we find that a standard Transformer with non-parametric queries and Fourier positional embeddings is competitive with specialized architectures that employ libraries of 3D-specific operators with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge. Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research.
Lipschitz Continuity Guided Knowledge Distillation
Authors: Bin Duan, Ziliang Zong, Liqiang Nie, Yan Yan Yuzhang Shang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although great success has been achieved by prior distillation methods via delicately designing various types of knowledge, they overlook the functional properties of neural networks, which makes the process of applying those techniques to new tasks unreliable and non-trivial. To alleviate such problem, in this paper, we initially leverage Lipschitz continuity to better represent the functional characteristic of neural networks and guide the knowledge distillation process. In particular, we propose a novel Lipschitz Continuity Guided Knowledge Distillation framework to faithfully distill knowledge by minimizing the distance between two neural networks' Lipschitz constants, which enables teacher networks to better regularize student networks and improve the corresponding performance. We derive an explainable approximation algorithm with an explicit theoretical derivation to address the NP-hard problem of calculating the Lipschitz constant. Experimental results have shown that our method outperforms other benchmarks over several knowledge distillation tasks (e.g., classification, segmentation and object detection) on CIFAR-100, ImageNet, and PASCAL VOC datasets.
Instance Similarity Learning for Unsupervised Feature Representation
Authors: Yunsong Wang, Ziyi Wu, Jiwen Lu, Jie Zhou Ziwei Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation. Conventional methods assign close instance pairs in the feature space with high similarity, which usually leads to wrong pairwise relationship for large neighborhoods because the Euclidean distance fails to depict the true semantic similarity on the feature manifold. On the contrary, our method mines the feature manifold in an unsupervised manner, through which the semantic similarity among instances is learned in order to obtain discriminative representations. Specifically, we employ the Generative Adversarial Networks (GAN) to mine the underlying feature manifold, where the generated features are applied as the proxies to progressively explore the feature manifold so that the semantic similarity among instances is acquired as reliable pseudo supervision. Extensive experiments on image classification demonstrate the superiority of our method compared with the state-of-the-art methods. The code is available at https://github.com/ZiweiWangTHU/ISL.git.
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives
Authors: Necati Cihan Camgoz, Richard Bowden Ben Saunders
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: It is common practice to represent spoken languages at their phonetic level. However, for sign languages, this implies breaking motion into its constituent motion primitives. Avatar based Sign Language Production (SLP) has traditionally done just this, building up animation from sequences of hand motions, shapes and facial expressions. However, more recent deep learning based solutions to SLP have tackled the problem using a single network that estimates the full skeletal structure.  We propose splitting the SLP task into two distinct jointly-trained sub-tasks. The first translation sub-task translates from spoken language to a latent sign language representation, with gloss supervision. Subsequently, the animation sub-task aims to produce expressive sign language sequences that closely resemble the learnt spatio-temporal representation. Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MoMP) architecture for sign language animation. A set of distinct motion primitives are learnt during training, that can be temporally combined at inference to animate continuous sign language sequences. We evaluate on the challenging RWTH-PHOENIX-Weather-2014T(PHOENIX14T) dataset, presenting extensive ablation studies and showing that MoMP outperforms baselines in user evaluations. We achieve state-of-the-art back translation performance with an 11% improvement over competing results. Importantly, and for the first time, we showcase stronger performance for a full translation pipeline going from spoken language to sign, than from gloss to sign.
DocFormer: End-to-End Transformer for Document Understanding
Authors: Bhavan Jasani, Bhargava Urala Kota, Yusheng Xie, R. Manmatha Srikar Appalaraju
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present DocFormer - a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats(forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters)
Spatially-Adaptive Image Restoration Using Distortion-Guided Networks
Authors: Maitreya Suin, A. N. Rajagopalan, Vishnu Naresh Boddeti Kuldeep Purohit
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a general learning-based solution for restoring images suffering from spatially-varying degradations. Prior approaches are typically degradation-specific and employ the same processing across different images and different pixels within. However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image. To overcome this limitation, we propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts computation to difficult regions in the image. SPAIR comprises of two components, (1) a localization network that identifies degraded pixels, and (2) a restoration network that exploits knowledge from the localization network in filter and feature domain to selectively and adaptively restore degraded pixels. Our key idea is to exploit the non-uniformity of heavy degradations in spatial-domain and suitably embed this knowledge within distortion-guided modules performing sparse normalization, feature extraction and attention. Our architecture is agnostic to physical formation model and generalizes across several types of spatially-varying degradations. We demonstrate the efficacy of SPAIR individually on four restoration tasks- removal of rain-streaks, raindrops, shadows and motion blur. Extensive qualitative and quantitative comparisons with prior art on 11 benchmark datasets demonstrate that our degradation-agnostic network design offers significant performance gains over state-of-the-art degradation-specific architectures. Code available at https://github.com/human-analysis/spatially-adaptive-image-restoration.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
Authors: Guorong Li, Zhenjun Han, Weigang Zhang, Yifan Yang, Qingming Huang, Nicu Sebe Xinyan Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Crowd counting is a difficult task because of the diversity of scenes. Most of the existing crowd counting methods adopt complex structures with massive backbones to enhance the generalization ability. Unfortunately, the performance of existing methods on large-scale data sets is not satisfactory. In order to handle various scenarios with less complex network, we explored how to efficiently use the multi-expert model for crowd counting tasks. We mainly focus on how to train more efficient expert networks and how to choose the most suitable expert. Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network. Similar samples are considered as a cluster which is used to obtain parameters of an expert. Besides, to make better use of the proposed method, we design a simple network called FPN with Deconvolution Counting Network, which is a more suitable base model for the multi-expert counting network. Experimental results show that multiple experts FDC (MFDC) achieves the best performance on four public data sets, including the large scale NWPU-Crowd data set. Furthermore, the MFDC trained on an extensive dense crowd data set can generalize well on the other data sets without extra training or fine-tuning.
Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction
Authors: Le Wang, Sanping Zhou, Wei Tang, Zhenxing Niu, Nanning Zheng, Gang Hua Fang Zheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people's diverse reaction patterns toward traffic agents in different categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods.
Multi-Scale Matching Networks for Semantic Correspondence
Authors: Ziyang Song, Zhenghao Ji, Gangming Zhao, Weifeng Ge, Yizhou Yu Dongyang Zhao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep features have been proven powerful in building accurate dense semantic correspondences in various previous works. However, the multi-scale and pyramidal hierarchy of convolutional neural networks has not been well studied to learn discriminative pixel-level features for semantic correspondence. In this paper, we propose a multiscale matching network that is sensitive to tiny semantic differences between neighboring pixels. We follow the coarse-to-fine matching strategy, and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks. During feature enhancement, intra-scale enhancement fuses same-resolution feature maps from multiple layers together via local self-attention, and cross-scale enhancement hallucinates higher resolution feature maps along the top-down hierarchy. Besides, we learn complementary matching details at different scales, and thus the overall matching score is refined by features at different semantic levels gradually. Our multi-scale matching network can be trained end-to-end easily with few additional learnable parameters. Experimental results demonstrate the proposed method achieves state-of-the-art performance on three popular benchmarks with high computational efficiency.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Authors: Enis Simsar, Ezgi Gülperi Er, Pinar Yanardag Oğuz Kaan Yüksel
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such directions is often done in a supervised or semi-supervised manner and requires manual annotations which limits their use in practice. In comparison, unsupervised discovery allows finding subtle directions that are difficult to detect a priori. In this work, we propose a contrastive learning-based approach to discover semantic directions in the latent space of pre-trained GANs in a self-supervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.
Few-Shot Visual Relationship Co-Localization
Authors: Vaibhav Mishra, Mayank Maheshwari, Anand Mishra Revant Teotia
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this paper, given a small bag of images, each containing a common but latent predicate, we are interested in localizing visual subject-object pairs connected via the common predicate in each of the images. We refer to this novel problem as visual relationship co-localization or VRC as an abbreviation. VRC is a challenging task, even more so than the well-studied object co-localization task. This becomes further challenging when using just a few images, the model has to learn to co-localize visual subject-object pairs connected via unseen predicates. To solve VRC, we propose an optimization framework to select a common visual relationship in each image of the bag. The goal of the optimization framework is to find the optimal solution by learning visual relationship similarity across images in a few-shot setting. To obtain robust visual relationship representation, we utilize a simple yet effective technique that learns relationship embedding as a translation vector from visual subject to visual object in a shared space. Further, to learn visual relationship similarity, we utilize a proven meta-learning technique commonly used for few-shot classification tasks. Finally, to tackle the combinatorial complexity challenge arising from an exponential number of feasible solutions, we use a greedy approximation inference algorithm that selects approximately the best solution. We extensively evaluate our proposed framework on variations of bag sizes obtained from two challenging public datasets, namely VrR-VG and VG-150, and achieve impressive visual co-localization performance.
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
Authors: Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, Yong Liu Tianxin Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.
Towards Better Explanations of Class Activation Mapping
Authors: Youngrock Oh Hyungsik Jung
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Increasing demands for understanding the internal behavior of convolutional neural networks (CNNs) have led to remarkable improvements in explanation methods. Particularly, several class activation mapping (CAM) based methods, which generate visual explanation maps by a linear combination of activation maps from CNNs, have been proposed. However, the majority of the methods lack a clear theoretical basis on how they assign the coefficients of the linear combination. In this paper, we revisit the intrinsic linearity of CAM with respect to the activation maps; we construct an explanation model of CNN as a linear function of binary variables that denote the existence of the corresponding activation maps. With this approach, the explanation model can be determined by additive feature attribution methods in an analytic manner. We then demonstrate the adequacy of SHAP values, which is a unique solution for the explanation model with a set of desirable properties, as the coefficients of CAM. Since the exact SHAP values are unattainable, we introduce an efficient approximation method, LIFT-CAM, based on DeepLIFT. Our proposed LIFT-CAM can estimate the SHAP values of the activation maps with high speed and accuracy. Furthermore, it greatly outperforms other previous CAM-based methods in both qualitative and quantitative aspects.
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
Authors: Jiaxing Huang, Aoran Xiao, Shijian Lu Dayan Guan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Video semantic segmentation is an essential task for the analysis and understanding of videos. Recent efforts largely focus on supervised video segmentation by learning from fully annotated data, but the learnt models often experience clear performance drop while applied to videos of a different domain. This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos. DA-VSN consists of two novel and complementary designs. The first is cross-domain TCR that guides the prediction of target frames to have similar temporal consistency as that of source frames (learnt from annotated source data) via adversarial learning. The second is intra-domain TCR that guides unconfident predictions of target frames to have similar temporal consistency as confident predictions of target frames. Extensive experiments demonstrate the superiority of our proposed domain adaptive video segmentation network which outperforms multiple baselines consistently by large margins.
PR-Net: Preference Reasoning for Personalized Video Highlight Detection
Authors: Penghao Zhou, Wenzhe Wang, Nenglun Chen, Pai Peng, Xing Sun, Wenping Wang Runnan Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention. Current methods regard the user's history as holistic information to predict the user's preference but negating the inherent diversity of the user's interests, resulting in vague preference representation. In this paper, we propose a simple yet efficient preference reasoning framework (PR-Net) to explicitly take the diverse interests into account for frame-level highlight prediction. Specifically, distinct user-specific preferences for each input query frame are produced, presented as the similarity weighted sum of history highlights to the corresponding query frame. Next, distinct comprehensive preferences are formed by the user-specific preferences and a learnable generic preference for more overall highlight measurement. Lastly, the degree of highlight and non-highlight for each query frame is calculated as semantic similarity to its comprehensive and non-highlight preferences, respectively. Besides, to alleviate the ambiguity due to the incomplete annotation, a new bi-directional contrastive loss is proposed to ensure a compact and differentiable metric space. In this way, our method significantly outperforms state-of-the-art methods with a relative improvement of 12% in mean accuracy precision.
PoinTr: Diverse Point Cloud Completion With Geometry-Aware Transformers
Authors: Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou Xumin Yu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ the transformers for point cloud generation. To facilitate transformers to better leverage the inductive bias about 3D geometric structures of point clouds, we further devise a geometry-aware block that models the local geometric relationships explicitly. The migration of transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Furthermore, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research. Experimental results show that our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
Learn-To-Race: A Multimodal Control Environment for Autonomous Racing
Authors: Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, Eric Nyberg James Herman
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information|from virtual cameras to a comprehensive array of inertial measurement sensors. Our environment, which includes a simulator and an interfacing training framework, accurately models vehicle dynamics and racing conditions. In this paper, we release the Arrival simulator for autonomous racing. Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-style racing, and multimodal trajectory prediction for autonomous driving. Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks. Finally, we provide an official L2R task dataset of expert demonstrations, as well as a series of baseline experiments and reference implementations. We make all code available: https://github.com/learn-to-race/l2r.
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
Authors: Weichao Zhao, Wengang Zhou, Yuechen Wang, Houqiang Li Hezhen Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Hand gesture serves as a critical role in sign language. Current deep-learning-based sign language recognition (SLR) methods may suffer insufficient interpretability and overfitting due to limited sign data sources. In this paper, we introduce the first self-supervised pre-trainable SignBERT with incorporated hand prior for SLR. SignBERT views the hand pose as a visual token, which is derived from an off-the-shelf pose extractor. The visual tokens are then embedded with gesture state, temporal and hand chirality information. To take full advantage of available sign data sources, SignBERT first performs self-supervised pre-training by masking and reconstructing visual tokens. Jointly with several mask modeling strategies, we attempt to incorporate hand prior in a model-aware method to better model hierarchical context over the hand sequence. Then with the prediction head added, SignBERT is fine-tuned to perform the downstream SLR task. To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i.e., NMFs-CSL, SLR500, MSASL and WLASL. Experiment results demonstrate the effectiveness of both self-supervised learning and imported hand prior. Furthermore, we achieve state-of-the-art performance on all benchmarks with a notable gain.
Improving Low-Precision Network Quantization via Bin Regularization
Authors: Dong Li, Ji Liu, Lu Tian, Yi Shan Tiantian Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Model quantization is an important mechanism for energy-efficient deployment of deep neural networks on resource-constrained devices by reducing the bit precision of weights and activations. However, it remains challenging to maintain high accuracy as bit precision decreases, especially for low-precision networks (e.g., 2-bit MobileNetV2). Existing methods have explored to address this problem by minimizing the quantization error or mimicking the data distribution of full-precision networks. In this work, we propose a novel weight regularization algorithm for improving low-precision network quantization. Instead of constraining the overall data distribution, we separably optimize all elements in each quantization bin to be as close to the target quantized value as possible. Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally. Experiments demonstrate that our method achieves consistent improvements over the state-of-the-art quantization-aware training methods for different low-precision networks. Particularly, our bin regularization improves LSQ for 2-bit MobileNetV2 and MobileNetV3-Small by 3.9% and 4.9% top-1 accuracy on ImageNet, respectively.
Probabilistic Modeling for Human Mesh Recovery
Authors: Georgios Pavlakos, Dinesh Jayaraman, Kostas Daniilidis Nikos Kolotouros
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: This paper focuses on the problem of 3D human reconstruction from 2D evidence. Although this is an inherently ambiguous problem, the majority of recent works avoid the uncertainty modeling and typically regress a single estimate for a given input. In contrast to that, in this work, we propose to embrace the reconstruction ambiguity and we recast the problem as learning a mapping from the input to a distribution of plausible 3D poses. Our approach is based on the normalizing flows model and offers a series of advantages. For conventional applications, where a single 3D estimate is required, our formulation allows for efficient mode computation. Using the mode leads to performance that is comparable with the state of the art among deterministic unimodal regression models. Simultaneously, since we have access to the likelihood of each sample, we demonstrate that our model is useful in a series of downstream tasks, where we leverage the probabilistic nature of the prediction as a tool for more accurate estimation. These tasks include reconstruction from multiple uncalibrated views, as well as human model fitting, where our model acts as a powerful image-based prior for mesh recovery. Our results validate the importance of probabilistic modeling, and indicate state-of-the-art performance across a variety of settings. Code and models are available at: https://www.seas.upenn.edu/ nkolot/projects/prohmr.
Distilling Virtual Examples for Long-Tailed Recognition
Authors: Jianxin Wu, Xiu-Shen Wei Yin-Yin He
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method. Specifically, by treating the predictions of a teacher model as virtual exam- ples, we prove that distilling from these virtual examples is equivalent to label distribution learning under certain con- straints. We show that when the virtual example distribu- tion becomes flatter than the original input distribution, the under-represented tail classes will receive significant im- provements, which is crucial in long-tailed recognition. The proposed DiVE method can explicitly tune the virtual exam- ple distribution to become flat. Extensive experiments on three benchmark datasets, including the large-scale iNat- uralist ones, justify that the proposed DiVE method can significantly outperform state-of-the-art methods. Further- more, additional analyses and experiments verify the virtual example interpretation, and demonstrate the effectiveness of tailored designs in DiVE for long-tailed problems.
Understanding and Mitigating Annotation Bias in Facial Expression Recognition
Authors: Jungseock Joo Yunliang Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The performance of a computer vision model depends on the size and quality of its training data. Recent studies have unveiled previously-unknown composition biases in common image datasets which then lead to skewed model outputs, and have proposed methods to mitigate these biases. However, most existing works assume that human-generated annotations can be considered gold-standard and unbiased. In this paper, we reveal that this assumption can be problematic, and that special care should be taken to prevent models from learning such annotation biases. We focus on facial expression recognition and compare the label biases between lab-controlled and in-the-wild datasets. We demonstrate that many expression datasets contain significant annotation biases between genders, especially when it comes to the happy and angry expressions, and that traditional methods cannot fully mitigate such biases in trained models. To remove expression annotation bias, we propose an AU-Calibrated Facial Expression Recognition (AUC-FER) framework that utilizes facial action units (AUs) and incorporates the triplet loss into the objective function. Experimental results suggest that the proposed method is more effective in removing expression annotation bias than existing techniques.
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination
Authors: Jinwoo Kim, Seonghyeon Nam, Dongwoo Lee, Yeonkyung Lee, Nahyup Kang, Hyong-Euk Lee, ByungIn Yoo, Jae-Joon Han, Seon Joo Kim Dongyoung Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We introduce a Large Scale Multi-Illuminant (LSMI) Dataset that contains 7,486 images, captured with three different cameras on more than 2,700 scenes with two or three illuminants. For each image in the dataset, the new dataset provides not only the pixel-wise ground truth illumination but also the chromaticity of each illuminant in the scene and the mixture ratio of illuminants per pixel. Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination. Therefore, our dataset captures natural composition in the real-world setting with wide field-of-view, providing more extensive dataset compared to existing datasets for multi-illumination white balance. As conventional single illuminant white balance algorithms cannot be directly applied, we also apply per-pixel DNN-based white balance algorithm and show its effectiveness against using patch-wise white balancing. We validate the benefits of our dataset through extensive analysis including a user-study, and expect the dataset to make meaningful contribution for future work in white balancing.
Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization
Authors: Zhipeng Zhang, Bin Xiao Xiuli Bi
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: When many forged images become more and more realistic with the help of image editing tools and deep learning techniques, authenticators need to improve their ability to verify these forged images. The process of generating and detecting forged images is thus similar to the principle of Generative Adversarial Networks (GANs). Creating realistic forged images requires a retouching process to suppress tampering artifacts and keep structural information. We view this retouching process as image style transfer and then proposed the fake-to-realistic transformation generator GT. For detecting the tampered regions, a forgery localization generator GM is proposed based on a multi-decoder-single-task strategy. By adversarial training two generators, the proposed alpha-learnable whitening and coloring transformation (alpha-learnable WCT) block in GT automatically suppresses the tampering artifacts in the forged images. Meanwhile, the detection and localization abilities of GM will be improved by learning the forged images retouched by GT. The experimental results demonstrate that the proposed two generators in GAN can simulate confrontation between fakers and authenticators well. The localization generator GM outperforms the state-of-the-art methods in splicing forgery detection and localization on four public datasets.
Learning To Regress Bodies From Images Using Differentiable Semantic Rendering
Authors: Nikos Athanasiou, Muhammed Kocabas, Michael J. Black Sai Kumar Dwivedi
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Learning to regress 3D human body shape and pose (e.g. SMPL parameters) from monocular images typically exploits losses on 2D keypoints, silhouettes, and/or part-segmentation when 3D training data is not available. Such losses, however, are limited because 2D keypoints do not supervise body shape and segmentations of people in clothing do not match projected minimally-clothed SMPL shapes. To exploit richer image information about clothed people, we introduce higher-level semantic information about clothing to penalize clothed and non-clothed regions of the image differently. To do so, we train a body regressor using a novel "Differentiable Semantic Rendering - DSR" loss. For Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight match between a rendered SMPL body and the minimally-clothed regions of the image. For clothed regions, we define the DSR-C loss to encourage the rendered SMPL body to be inside the clothing mask. To ensure end-to-end differentiable training, we learn a semantic clothing prior for SMPL vertices from thousands of clothed human scans. We perform extensive qualitative and quantitative experiments to evaluate the role of clothing semantics on the accuracy of 3D human pose and shape estimation. We outperform all previous state-of-the-art methods on 3DPW and Human3.6M and obtain on par results on MPI-INF-3DHP. Code and trained models will be available for research at https://dsr.is.tue.mpg.de/
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
Authors: Mohsen Rohani, Jun Luo Amir Rasouli
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose BiPed, a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multimodal data. Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations. Using public pedestrian behavior benchmark datasets for driving, PIE and JAAD, we highlight the benefits of the proposed method for behavior prediction and show that our model achieves state-of-the-art performance and improves trajectory and action prediction by up to 22% and 9% respectively. We further investigate the contributions of the proposed reasoning techniques via extensive ablation studies.
Learning Target Candidate Association To Keep Track of What Not To Track
Authors: Martin Danelljan, Danda Pani Paudel, Luc Van Gool Christoph Mayer
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The presence of objects that are confusingly similar to the tracked target, poses a fundamental challenge in appearance-based visual tracking. Such distractor objects are easily misclassified as the target itself, leading to eventual tracking failure. While most methods strive to suppress distractors through more powerful appearance models, we take an alternative approach. We propose to keep track of distractor objects in order to continue tracking the target. To this end, we introduce a learned association network, allowing us to propagate the identities of all target candidates from frame-to-frame. To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision. We conduct comprehensive experimental validation and analysis of our approach on several challenging datasets. Our tracker sets a new state-of-the-art on six benchmarks, achieving an AUC score of 67.1% on LaSOT and a +5.8% absolute gain on the OxUvA long-term dataset.
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction
Authors: Sunghoon Im, Francois Rameau, Minjun Kang, In So Kweon Jaesung Choe
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion. Recent studies concentrate on deep neural architectures for depth estimation by using conventional depth fusion method or direct 3D reconstruction network by regressing Truncated Signed Distance Function (TSDF). In this paper, we advocate that replicating the traditional two stages framework with deep neural networks improves both the interpretability and the accuracy of the results. As mentioned, our network operates in two steps: 1) the local computation of the local depth maps with a deep MVS technique, and, 2) the depth maps and images' features fusion to build a single TSDF volume. In order to improve the matching performance between images acquired from very different viewpoints (e.g., large-baseline and rotations), we introduce a rotation-invariant 3D convolution kernel called PosedConv. The effectiveness of the proposed architecture is underlined via a large series of experiments conducted on the ScanNet dataset where our approach compares favorably against both traditional and deep learning techniques.
Black-Box Detection of Backdoor Attacks With Limited Information and Data
Authors: Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu Yinpeng Dong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments. A malicious backdoor could be embedded in a model by poisoning the training dataset, whose intention is to make the infected model give wrong predictions during inference when the specific trigger appears. To mitigate the potential threats of backdoor attacks, various backdoor detection and defense methods have been proposed. However, the existing techniques usually require the poisoned training data or access to the white-box model, which is commonly unavailable in practice. In this paper, we propose a black-box backdoor detection (B3D) method to identify backdoor attacks with only query access to the model. We introduce a gradient-free optimization algorithm to reverse-engineer the potential trigger for each class, which helps to reveal the existence of backdoor attacks. In addition to backdoor detection, we also propose a simple strategy for reliable predictions using the identified backdoored models. Extensive experiments on hundreds of DNN models trained on several datasets corroborate the effectiveness of our method under the black-box setting against various backdoor attacks.
A Robust Loss for Point Cloud Registration
Authors: Yuxin Yao, Bailin Deng, Juyong Zhang Zhi Deng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The performance of surface registration relies heavily on the metric used for the alignment error between the source and target shapes. Traditionally, such a metric is based on the point-to-point or point-to-plane distance from the points on the source surface to their closest points on the target surface, which is susceptible to failure due to instability of the closest-point correspondence. In this paper, we propose a novel metric based on the intersection points between the two shapes and a random straight line, which does not assume a specific correspondence. We verify the effectiveness of this metric by extensive experiments, including its direct optimization for a single registration problem as well as unsupervised learning for a set of registration problems. The results demonstrate that the algorithms utilizing our proposed metric outperforms the state-of-the-art optimization-based and unsupervised learning-based methods.
Semantic Concentration for Domain Adaptation
Authors: Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li Shuang Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Domain adaptation (DA) paves the way for label annotation and dataset bias issues by the knowledge transfer from a label-rich source domain to a related but unlabeled target domain. A mainstream of DA methods is to align the feature distributions of the two domains. However, the majority of them focus on the entire image features where irrelevant semantic information, e.g., the messy background, is inevitably embedded. Enforcing feature alignments in such case will negatively influence the correct matching of objects and consequently lead to the semantically negative transfer due to the confusion of irrelevant semantics. To tackle this issue, we propose Semantic Concentration for Domain Adaptation (SCDA), which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions. Specifically, we train the classifier to class-wisely maximize the prediction distribution divergence of each sample pair, which enables the model to find the region with large differences among the same class of samples. Meanwhile, the feature extractor attempts to minimize that discrepancy, which suppresses the features of dissimilar regions among the same class of samples and accentuates the features of principal parts. As a general method, SCDA can be easily integrated into various DA methods as a regularizer to further boost their performance. Extensive experiments on the cross-domain benchmarks show the efficacy of SCDA.
PICCOLO: Point Cloud-Centric Omnidirectional Localization
Authors: Changwoon Choi, Hojun Jang, Young Min Kim Junho Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present PICCOLO, a simple and efficient algorithm for omnidirectional localization. Given a colored point cloud and a 360 panorama image of a scene, our objective is to recover the camera pose at which the panorama image is taken. Our pipeline works in an off-the-shelf manner with a single image given as a query and does not require any training of neural networks or collecting ground-truth poses of images. Instead, we match each point cloud color to the holistic view of the panorama image with gradient-descent optimization to find the camera pose. Our loss function, called sampling loss, is point cloud-centric, evaluated at the projected location of every point in the point cloud. In contrast, conventional photometric loss is image-centric, comparing colors at each pixel location. With a simple change in the compared entities, sampling loss effectively overcomes the severe visual distortion of omnidirectional images, and enjoys the global context of the 360 view to handle challenging scenarios for visual localization. PICCOLO outperforms existing omnidirectional localization algorithms in both accuracy and stability when evaluated in various environments.