mtybilly/Instruct2DS huggingface dataset:
End-to-End Video Instance Segmentation via Spatial-Temporal Graph Neural Networks
Authors: Ning Xu, Kean Chen, Weiyao Lin Tao Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Video instance segmentation is a challenging task that extends image instance segmentation to the video domain. Existing methods either rely only on single-frame information for the detection and segmentation subproblems or handle tracking as a separate post-processing step, which limit their capability to fully leverage and share useful spatial-temporal information for all the subproblems. In this paper, we propose a novel graph-neural-network (GNN) based method to handle the aforementioned limitation. Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations are used for tracking. Both inter and intra-frame information is effectively propagated and shared via graph updates and all the subproblems (i.e. detection, segmentation and tracking) are jointly optimized in an unified framework. The performance of our method shows great improvement on the YoutubeVIS validation dataset compared to existing methods and achieves 36.5% AP with a ResNet-50 backbone, operating at 22 FPS.
The Surprising Impact of Mask-Head Architecture on Novel Class Segmentation
Authors: Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang Vighnesh Birodkar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Instance segmentation models today are very accurate when trained on large annotated datasets, but collecting mask annotations at scale is prohibitively expensive. We address the partially supervised instance segmentation problem in which one can train on (significantly cheaper) bounding boxes for all categories but use masks only for a subset of categories. In this work, we focus on a popular family of models which apply differentiable cropping to a feature map and predict a mask based on the resulting crop. Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes. This training strategy also allows us to take advantage of alternative mask-head architectures, which we exploit by replacing the typical mask-head of 2-4 layers with significantly deeper off-the-shelf architectures (e.g. ResNet, Hourglass models). While many of these architectures perform similarly when trained in fully supervised mode, our main finding is that they can generalize to novel classes in dramatically different ways. We call this ability of mask-heads to generalize to unseen classes the strong mask generalization effect and show that without any specialty modules or losses, we can achieve state-of-the-art results in the partially supervised COCO instance segmentation benchmark. Finally, we demonstrate that our effect is general, holding across underlying detection methodologies (including anchor-based, anchor-free or no detector at all) and across different backbone networks. Code and pre-trained models are available at https://git.io/deepmac.
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
Authors: Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau Cheng Gu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Event cameras, inspired by biological vision systems, provide a natural and data efficient representation of visual information. Visual information is acquired in the form of events that are triggered by local brightness changes. However, because most brightness changes are triggered by relative motion of the camera and the scene, the events recorded at a single sensor location seldom correspond to the same world point. To extract meaningful information from event cameras, it is helpful to register events that were triggered by the same underlying world point. In this work we propose a new model of event data that captures its natural spatio-temporal structure. We start by developing a model for aligned event data. That is, we develop a model for the data as though it has been perfectly registered already. In particular, we model the aligned data as a spatio-temporal Poisson point process. Based on this model, we develop a maximum likelihood approach to registering events that are not yet aligned. That is, we find transformations of the observed events that make them as likely as possible under our model. In particular we extract the camera rotation that leads to the best event alignment. We show new state of the art accuracy for rotational velocity estimation on the DAVIS 240C dataset [??]. In addition, our method is also faster and has lower computational complexity than several competing methods.
Learning Self-Similarity in Space and Time As Generalized Motion for Video Action Recognition
Authors: Manjin Kim, Suha Kwak, Minsu Cho Heeseung Kwon
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this paper, we propose a rich and robust motion representation based on spatio-temporal self-similarity (STSS). Given a sequence of frames, STSS represents each local region as similarities to its neighbors in space and time. By converting appearance features into relational values, it enables the learner to better recognize structural patterns in space and time. We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it. The proposed neural block, dubbed SELFY, can be easily inserted into neural architectures and trained end-to-end without additional supervision. With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition. Our experimental analysis demonstrates its superiority over previous methods for motion modeling as well as its complementarity to spatio-temporal features from direct convolution. On the standard action recognition benchmarks, SomethingSomething-V1 & V2, Diving-48, and FineGym, the proposed method achieves the state-of-the-art results.
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation
Authors: Shaogang Gong Guile Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Contemporary domain generalization (DG) and multi-source unsupervised domain adaptation (UDA) methods mostly collect data from multiple domains together for joint optimization. However, this centralized training paradigm poses a threat to data privacy and is not applicable when data are non-shared across domains. In this work, we propose a new approach called Collaborative Optimization and Aggregation (COPA), which aims at optimizing a generalized target model for decentralized DG and UDA, where data from different domains are non-shared and private. Our base model consists of a domain-invariant feature extractor and an ensemble of domain-specific classifiers. In an iterative learning process, we optimize a local model for each domain, and then centrally aggregate local feature extractors and assemble domain-specific classifiers to construct a generalized global model, without sharing data from different domains. To improve generalization of feature extractors, we employ hybrid batch-instance normalization and collaboration of frozen classifiers. For better decentralized UDA, we further introduce a prediction agreement mechanism to overcome local disparities towards central model aggregation. Extensive experiments on five DG and UDA benchmark datasets show that COPA is capable of achieving comparable performance against the state-of-the-art DG and UDA methods without the need for centralized data collection in model training.
CR-Fill: Generative Image Inpainting With Auxiliary Contextual Reconstruction
Authors: Zhe Lin, Huchuan Lu, Vishal M. Patel Yu Zeng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent deep generative inpainting methods use attention layers to allow the generator to explicitly borrow feature patches from the known region to complete a missing region. Due to the lack of supervision signals for the correspondence between missing regions and known regions, it may fail to find proper reference features, which often leads to artifacts in the results. Also, it computes pair-wise similarity across the entire feature map during inference bringing a significant computational overhead. To address this issue, we propose to teach such patch-borrowing behavior to an attention-free generator by joint training of an auxiliary contextual reconstruction task, which encourages the generated output to be plausible even when reconstructed by surrounding regions. The auxiliary branch can be seen as a learnable loss function, i.e. named as contextual reconstruction (CR) loss, where query-reference feature similarity and reference-based reconstructor are jointly optimized with the inpainting generator. The auxiliary branch (i.e. CR loss) is required only during training, and only the inpainting generator is required during the inference. Experimental results demonstrate that the proposed inpainting model compares favourably against the state-of-the-art in terms of quantitative and visual performance. Code is available at https://github.com/zengxianyu/crfill.
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Authors: Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun Alexander Cui
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations. In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that covers a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models. Unlike previous work in diverse motion forecasting, our diversity objective explicitly rewards sampling future scenarios that require distinct reactions from the self-driving vehicle for improved safety. Our contingency planner then finds comfortable and non-conservative trajectories that ensure safe reactions to a wide range of future scenarios. Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and less conservative motion plans in long-term closed-loop simulations when compared to current state-of-the-art models.
Causal Attention for Unbiased Visual Recognition
Authors: Chang Zhou, Qianru Sun, Hanwang Zhang Tan Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a "dog" model is learned within "grass+dog" and "road+dog" respectively, so the "grass" and "road" contexts will no longer confound the "dog" recognition. However, such annotation is not only prohibitively expensive, but also inherently problematic, as the confounders are elusive in nature. In this paper, we propose a causal attention module (CaaM) that self-annotates the confounders in unsupervised fashion. In particular, multiple CaaMs can be stacked and integrated in conventional attention CNN and self-attention Vision Transformer. In OOD settings, deep models with CaaM outperform those without it significantly; even in IID settings, the attention localization is also improved by CaaM, showing a great potential in applications that require robust visual saliency. Codes are available at https://github.com/Wangt-CN/CaaM.
EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS
Authors: Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji Qinqin Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Based on the relaxed search space, differential architecture search (DARTS) is efficient in searching for a high-performance architecture. However, the unbalanced competition among operations that have different trainable parameters causes the model collapse. Besides, the inconsistent structures in the search and retraining stages causes cross-stage evaluation to be unstable. In this paper, we call these issues as an operation gap and a structure gap in DARTS. To shrink these gaps, we propose to induce equalized and consistent optimization in differentiable architecture search (EC-DARTS). EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked. Besides, we introduce an induced structural transition to bridge the structure gap between the model structures in the search and retraining stages. Extensive experiments on CIFAR10 and ImageNet demonstrate the effectiveness of our method. Specifically, on CIFAR10, we achieve a test error of 2.39%, while only 0.3 GPU days on NVIDIA TITAN V. On ImageNet, our method achieves a top-1 error of 23.6% under the mobile setting.
Detecting Persuasive Atypicality by Modeling Contextual Compatibility
Authors: Rebecca Hwa, Adriana Kovashka Meiqi Guo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We propose a new approach to detect atypicality in persuasive imagery. Unlike atypicality which has been studied in prior work, persuasive atypicality has a particular purpose to convey meaning, and relies on understanding the common-sense spatial relations of objects. We propose a self-supervised attention-based technique which captures contextual compatibility, and models spatial relations in a precise manner. We further experiment with capturing common sense through the semantics of co-occurring object classes. We verify our approach on a dataset of atypicality in visual advertisements, as well as a second dataset capturing atypicality that has no persuasive intent.
Warp-Refine Propagation: Semi-Supervised Auto-Labeling via Cycle-Consistency
Authors: Alexis Vallet, Yasunori Kudo, Shin-ichi Maeda, Tommi Kerola, Rares Ambrus, Dennis Park, Adrien Gaidon Aditya Ganeshan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets. Labelling is a tedious process that can take hours per image. Automatically annotating video sequences by propagating sparsely labeled frames through time is a more scalable alternative. In this work, we propose a novel label propagation method, termed Warp-Refine Propagation, that combines semantic cues with geometric cues to efficiently auto-label videos. Our method learns to refine geometrically-warped labels and infuse them with learned semantic priors in a semi-supervised setting by leveraging cycle consistency across time. We quantitatively show that our method improves label-propagation by a noteworthy margin of 13.1 mIoU on the ApolloScape dataset. Furthermore, by training with the auto-labelled frames, we achieve competitive results on three semantic-segmentation benchmarks, improving the state-of-the-art by a large margin of 1.8 and 3.61 mIoU on NYU-V2 and KITTI, while matching the current best results on Cityscapes.
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Authors: Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon Jooyoung Choi
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles.
STVGBert: A Visual-Linguistic Transformer Based Framework for Spatio-Temporal Video Grounding
Authors: Qian Yu, Dong Xu Rui Su
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Spatio-temporal video grounding (STVG) aims to localize a spatio-temporal tube of a target object in an untrimmed video based on a query sentence. In this work, we propose a one-stage visual-linguistic transformer based framework called STVGBert for the STVG task, which can simultaneously localize the target object in both spatial and temporal domains. Specifically, without resorting to pre-generated object proposals, our STVGBert directly takes a video and a query sentence as the input, and then produces the cross-modal features by using the newly introduced cross-modal feature learning module ST-ViLBert. Based on the cross-modal features, our method then generates bounding boxes and predicts the starting and ending frames to produce the predicted object tube. To the best of our knowledge, our STVGBert is the first one-stage method, which can handle the STVG task without relying on any pre-trained object detectors. Comprehensive experiments demonstrate our newly proposed framework outperforms the state-of-the-art multi-stage methods on two benchmark datasets Vid-STG and HC-STVG.
Universal Representation Learning From Multiple Domains for Few-Shot Classification
Authors: Xialei Liu, Hakan Bilen Wei-Hong Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this paper, we look at the problem of few-shot image classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use various adaptation strategies for aligning their visual representations to new domains or select the relevant ones from multiple domain-specific feature extractors. In this work, we present URL, which learns a single set of universal visual representations by distilling knowledge of multiple domain-specific networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient.
Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning
Authors: Jie Geng, Wen Jiang, Xinyang Deng, Zhe Xu Kai Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This work presents a task-unified confidence estimation approach for semi-supervised few-shot learning, named pseudo-loss confidence metric (PLCM). It measures the data credibility by the loss distribution of pseudo-labels, which is synthetical considered multi-tasks. Specifically, pseudo-labeled data of different tasks are mapped to a unified metric space by mean of the pseudo-loss model, making it possible to learn the prior pseudo-loss distribution. Then, confidence of pseudo-labeled data is estimated according to the distribution component confidence of its pseudo-loss. Thus highly reliable pseudo-labeled data are selected to strengthen the classifier. Moreover, to overcome the pseudo-loss distribution shift and improve the effectiveness of classifier, we advance the multi-step training strategy coordinated with the class balance measures of class-apart selection and class weight. Experimental results on four popular benchmark datasets demonstrate that the proposed approach can effectively select pseudo-labeled data and achieve the state-of-the-art performance.
Learning Dual Priors for JPEG Compression Artifacts Removal
Authors: Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha Xueyang Fu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep learning (DL)-based methods have achieved great success in solving the ill-posed JPEG compression artifacts removal problem. However, as most DL architectures are designed to directly learn pixel-level mapping relationships, they largely ignore semantic-level information and lack sufficient interpretability. To address the above issues, in this work, we propose an interpretable deep network to learn both pixel-level regressive prior and semantic-level discriminative prior. Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively. The content-relevant prior is formulated as a DL-based image-to-image regressor to perform as a de-blocker from the pixel-level. The gradient-relevant prior serves as a DL-based classifier to distinguish whether the image is compressed from the semantic-level. To effectively solve the variational model, we design an alternating minimization algorithm and unfold it into a deep network architecture. In this way, not only the interpretability of the deep network is increased, but also the dual priors can be well estimated from training samples. By integrating the two priors into a single framework, the image de-blocking problem can be well-constrained, leading to a better performance. Experiments on benchmarks and real-world use cases demonstrate the superiority of our method to the existing state-of-the-art approaches.
Searching for Two-Stream Models in Multivariate Space for Video Recognition
Authors: Heng Wang, Mike Zheng Shou, Matt Feiszli, Zhangyang Wang, Zhicheng Yan Xinyu Gong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet, prescribe separate streams to learn complementary features, and achieve stronger performance. However, manually designing both streams as well as the in-between fusion blocks is a daunting task, requiring to explore a tremendously large design space. Such manual exploration is time-consuming and often ends up with sub-optimal architectures when computational resources are limited and the exploration is insufficient. In this work, we present a pragmatic neural architecture search approach, which is able to search for two-stream video models in giant spaces efficiently. We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models. Furthermore, we propose a progressive search procedure, by searching for the architecture of individual streams, fusion blocks and attention blocks one after the other. We demonstrate two-stream models with significantly better performance can be automatically discovered in our design space. Our searched two-stream models, namely Auto-TSNet, consistently outperform other models on standard benchmarks. On Kinetics, compared with the SlowFast model, our Auto-TSNet-L model reduces FLOPS by nearly 11 times while achieving the same accuracy 78.9%. On Something-Something-V2, Auto-TSNet-M improves the accuracy by at least 2% over other methods which use less than 50 GFLOPS per video.
Refining Activation Downsampling With SoftPool
Authors: Ronald Poppe, Grigorios Kalliatakis Alexandros Stergiou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Convolutional Neural Networks (CNNs) use pooling to decrease the size of activation maps. This process is crucial to increase the receptive fields and to reduce computational requirements of subsequent convolutions. An important feature of the pooling operation is the minimization of information loss, with respect to the initial activation maps, without a significant impact on the computation and memory overhead. To meet these requirements, we propose SoftPool: a fast and efficient method for exponentially weighted activation downsampling. Through experiments across a range of architectures and pooling methods, we demonstrate that SoftPool can retain more information in the reduced activation maps. This refined downsampling leads to improvements in a CNN's classification accuracy. Experiments with pooling layer substitutions on ImageNet1K show an increase in accuracy over both original architectures and other pooling methods. We also test SoftPool on video datasets for action recognition. Again, through the direct replacement of pooling layers, we observe consistent performance improvements while computational loads and memory requirements remain limited.
Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing
Authors: Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll Garvita Tiwari
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present Neural Generalized Implicit Functions(Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body(or clothing). However such models usually have fixed and limited resolutions, and require difficult data pre-processing steps, and cannot be used for complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and non-rigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvement over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code, and data publicly available.
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing
Authors: Chul Lee, Chang-Su Kim Jae-Han Lee
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks. An MTL model is trained to estimate multiple pixelwise predictions using an overall loss, which is a linear combination of individual task losses. The proposed algorithm dynamically adjusts the linear weights to learn all tasks effectively. Instead of controlling the trend of each loss value directly, we balance the loss scale --- the product of the loss value and its weight --- periodically. In addition, by evaluating the difficulty of each task based on the previous loss record, the proposed algorithm focuses more on difficult tasks during training. Experimental results show that the proposed algorithm outperforms conventional weighting algorithms for MTL of various pixelwise tasks. Codes are available at https://github.com/jaehanlee-mcl/LSB-MTL.
MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking
Authors: Qian Xie, Yu-Kun Lai, Jing Wu, Kun Long, Jun Wang Zhoutao Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Benefiting from the excellent performance of Siamese-based trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still under-explored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet.
ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot
Authors: Yizhou Wang, Jenq-Neng Hwang Jiarui Cai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i.e., either sacrifice the head's accuracy for better tail classification or elevate the head's accuracy even higher but ignore the tail. Existing algorithms bypass such trade-off by a multi-stage training process: pre-training on imbalanced set and fine-tuning on balanced set. Though achieving promising performance, not only are they sensitive to the generalizability of the pre-trained model, but also not easily integrated into other computer vision tasks like detection and segmentation, where pre-training of classifier solely is not applicable. In this paper, we propose a one-stage long-tailed recognition scheme, ally complementary experts (ACE), where the expert is the most knowledgeable specialist in a sub-set that dominates its training, and is complementary to other experts in the less-seen categories without disturbed by what it has never seen. We design a distribution-adaptive optimizer to adjust the learning pace of each expert to avoid over-fitting. Without special bells and whistles, the vanilla ACE outperforms the current one-stage SOTA method by 3 10% on CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist datasets. It is also shown to be the first one to break the "seesaw" trade-off by improving the accuracy of the majority and minority categories simultaneously in only one stage.
Hyperspectral Image Denoising With Realistic Data
Authors: Ying Fu, Cheng Li Tao Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The hyperspectral image (HSI) denoising has been widely utilized to improve HSI qualities. Recently, learning-based HSI denoising methods have shown their effectiveness, but most of them are based on synthetic dataset and lack the generalization capability on real testing HSI. Moreover, there is still no public paired real HSI denoising dataset to learn HSI denoising network and quantitatively evaluate HSI methods. In this paper, we mainly focus on how to produce realistic dataset for learning and evaluating HSI denoising network. On the one hand, we collect a paired real HSI denoising dataset, which consists of shortexposure noisy HSIs and the corresponding long-exposure clean HSIs. On the other hand, we propose an accurate HSI noise model which matches the distribution of real data well and can be employed to synthesize realistic dataset. On the basis of the noise model, we present an approach to calibrate the noise parameters of the given hyperspectral camera. The extensive experimental results show that a network learned with only synthetic data generated by our noise model performs as well as it is learned with paired real data.
Collaborative Learning With Disentangled Features for Zero-Shot Domain Adaptation
Authors: Jae-Pil Heo Won Young Jhoo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Typical domain adaptation techniques aim to transfer label information from a label-rich source domain to a label-scarce target domain in the same label space. However, it is often hard to get even the unlabeled target domain data of a task of interest. In such a case, we can capture the domain shift between the source domain and target domain from an unseen task and transfer it to the task of interest, which is known as zero-shot domain adaptation (ZSDA). Existing state-of-the-art methods for ZSDA attempted to generate target domain data. However, training such generative models causes significant computational overhead and is hardly optimized. In this paper, we propose a novel ZSDA method that learns a task-agnostic domain shift by collaborative training of domain-invariant semantic features and task-invariant domain features via adversarial learning. Meanwhile, the spatial attention map is learned from disentangled feature representations to selectively emphasize the domain-specific salient parts of the domain-invariant features. Experimental results show that our ZSDA method achieves state-of-the-art performance on several benchmarks.
Rethinking Noise Synthesis and Modeling in Raw Denoising
Authors: Hongwei Qin, Xiaogang Wang, Hongsheng Li Yi Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The lack of large-scale real raw image denoising dataset gives the rise to challenges on synthesizing realistic raw image noise for training denoising models. However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors. Existing methods are unable to model all noise sources accurately, and building a noise model for each sensor is also laborious. In this paper, we introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors. Two efficient and generic techniques: pattern-aligned patch sampling and high-bit reconstruction help accurate synthesis of spatial-correlated noise and high-bit noise respectively. We conduct systematic experiments on SIDD and ELD datasets. The results show that (1) our method outperforms existing methods and demonstrates wide generalization on different sensors and lighting conditions. (2) Recent conclusions derived from DNN-based noise modeling methods are actually based on inaccurate noise parameters. The DNN-based methods still cannot outperform physics-based statistical methods.
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective
Authors: Yali Li, Shengjin Wang Xuege Hou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: General face recognition has seen remarkable progress in recent years. However, large age gap still remains a big challenge due to significant alterations in facial appearance and bone structure. Disentanglement plays a key role in partitioning face representations into identity-dependent and age-dependent components for age-invariant face recognition (AIFR). In this paper we propose a multi-task learning framework based on mutual information minimization (MT-MIM), which casts the disentangled representation learning as an objective of information constraints. The method trains a disentanglement network to minimize mutual information between the identity component and age component of the face image from the same person, and reduce the effect of age variations during the identification process. For quantitative measure of the degree of disentanglement, we verify that mutual information can represent as metric. The resulting identity-dependent representations are used for age-invariant face recognition. We evaluate MT-MIM on popular public-domain face aging datasets (FG-NET, MORPH Album 2, CACD and AgeDB) and obtained significant improvements over previous state-of-the-art methods. Specifically, our method exceeds the baseline models by over 0.4% on MORPH Album 2, and over 0.7% on CACD subsets, which are impressive improvements at the high accuracy levels of above 99% and an average of 94%.
Contact-Aware Retargeting of Skinned Motion
Authors: Duygu Ceylan, Aaron Hertzmann, Jimei Yang, Jun Saito Ruben Villegas
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: This paper introduces a motion retargeting method that preserves self-contacts and prevents inter-penetration. Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts. Likewise, self-penetrations, such as a hand passing into the torso, are a typical artifact of motion estimation methods. The input to our method is a human motion sequence and a target skeleton and character geometry. The method identifies self-contacts and ground contacts in the input motion, and optimizes the motion to apply to the output skeleton, while preserving these contacts and reducing self-penetrations. We introduce a novel geometry-conditioned recurrent network with an encoder-space optimization strategy that achieves efficient retargeting while satisfying contact constraints. In experiments, our results quantitatively outperform previous methods and in the user study our retargeted motions are rated as higher-quality than those produced by recent works. We also show our method generalizes to motion estimated from human videos where we improve over previous works that produce noticeable interpenetration.
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds
Authors: Xu Yan, Jiantao Gao, Weibing Zhao, Wei Zhang, Zhen Li, Shuguang Cui Chaoda Zheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area. However, due to the common occlusion in LiDAR scans, it is non-trivial to conduct accurate feature comparisons on severe sparse and incomplete shapes. In this work, we exploit the ground truth bounding box given in the first frame as a strong cue to enhance the feature description of the target object, enabling a more accurate feature comparison in a simple yet effective way. In particular, we first propose the BoxCloud, an informative and robust representation, to depict an object using the point-to-box relation. We further design an efficient box-aware feature fusion module, which leverages the aforementioned BoxCloud for reliable feature matching and embedding. Integrating the proposed general components into an existing model P2B, we construct a superior box-aware tracker (BAT). Experiments confirm that our proposed BAT outperforms the previous state-of-the-art by a large margin on both KITTI and NuScenes benchmarks, achieving a 12.8% improvement in terms of precision while running  20% faster.
FATNN: Fast and Accurate Ternary Neural Networks
Authors: Bohan Zhuang, Chunhua Shen Peng Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. However, 2 bits are required to encode the ternary representation with only 3 quantization levels leveraged. As a result, conventional TNNs have similar memory consumption and speed compared with the standard 2-bit models, but have worse representational capability. Moreover, there is still a significant gap in accuracy between TNNs and full-precision networks, hampering their deployment to real applications. To tackle these two challenges, in this work, we first show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by 2x. Second, to mitigate the performance gap, we elaborately design an implementation-dependent ternary quantization algorithm. The proposed framework is termed Fast and Accurate Ternary Neural Networks (FATNN). Experiments on image classification demonstrate that our FATNN surpasses the state-of-the-arts by a significant margin in accuracy. More importantly, speedup evaluation compared with various precisions is analyzed on several platforms, which serves as a strong benchmark for further research.
Multitask AET With Orthogonal Tangent Regularity for Dark Object Detection
Authors: Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, Tatsuya Harada Ziteng Cui
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Dark environment becomes a challenge for computer vision algorithms owing to insufficient photons and undesirable noises. Most of the existing studies tackle this by either targeting human vision for better visual perception or improving the machine vision for specific high-level tasks. In addition, these methods rely on data argumentation and directly train their models based on real-world or over-simplified synthetic datasets without exploring the intrinsic pattern behind illumination translation. Here, we propose a novel multitask auto encoding transformation (MAET) model that combines human vision and machine vision tasks to enhance object detection in a dark environment. With a self-supervision learning, the MAET learns an intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation considering the physical noise model and image signal processing (ISP). Based on this representation, we achieve object detection task by decoding the bounding box coordinates and classes. To avoid the over-entanglement of two tasks, our MAET disentangles the object and degrading features by imposing an orthogonal tangent regularity. This forms a parametric manifold along which multitask predictions can be geometrically formulated by maximizing the orthogonality between the tangents along the outputs of respective tasks. Our framework can be implemented based on the mainstream object detection architecture and directly trained end-to-end using the normal target detection datasets, such as COCO and VOC. We have achieved the state-of-the-art performance using synthetic and real-world datasets.
Field-Guide-Inspired Zero-Shot Learning
Authors: Bharath Hariharan, Kavita Bala Utkarsh Mall
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Modern recognition systems require large amounts of supervision to achieve accuracy. Adapting to new domains requires significant data from experts, which is onerous and can become too expensive. Zero-shot learning requires an annotated set of attributes for a novel category. Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment. This is especially the case when the recognition domain is an expert domain. We introduce a new field-guide-inspired approach to zero-shot annotation where the learner model interactively asks for the most useful attributes that define a class. We evaluate our method on classification benchmarks with attribute annotations like CUB, SUN, and AWA2 and show that our model achieves the performance of a model with full annotations at the cost of a significantly fewer number of annotations. Since the time of experts is precious, decreasing annotation cost can be very valuable for real-world deployment.
Contrastive Attention Maps for Self-Supervised Co-Localization
Authors: Youngjung Uh, Junsuk Choe, Hyeran Byun Minsong Ki
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The goal of unsupervised co-localization is to locate the object in a scene under the assumptions that 1) the dataset consists of only one superclass, e.g., birds, and 2) there are no human-annotated labels in the dataset. The most recent method achieves impressive co-localization performance by employing self-supervised representation learning approaches such as predicting rotation. In this paper, we introduce a new contrastive objective directly on the attention maps to enhance co-localization performance. Our contrastive loss function exploits rich information of location, which induces the model to activate the extent of the object effectively. In addition, we propose a pixel-wise attention pooling that selectively aggregates the feature map regarding their magnitudes across channels. Our methods are simple and shown effective by extensive qualitative and quantitative evaluation, achieving state-of-the-art co-localization performances by large margins on four datasets: CUB-200-2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Our code will be publicly available online for the research community.