Tao Zhou, Huazhu Fu, Geng Chen, Yi Zhou, Deng-Ping Fan, Ling Shao

Abstract: RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing works often focus on learning a shared representation through various fusion strategies, with few methods explicitly considering how to preserve modality-specific characteristics. In this paper, taking a new perspective, we propose a specificity-preserving network for RGB-D saliency detection, which benefits saliency detection performance by exploring both the shared information and modality-specific properties (e.g., specificity). Specifically, two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. A cross-enhanced integration module (CIM) is proposed to fuse cross-modal features in the shared learning network, which are then propagated to the next layer for integrating cross-level information. Besides, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder, which can provide rich complementary multi-modal information to boost the saliency detection performance. Further, a skip connection is used to combine hierarchical features between the encoder and decoder layers. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods.

@InProceedings{Zhou_2021_ICCV,
    author    = {Zhou, Tao and Fu, Huazhu and Chen, Geng and Zhou, Yi and Fan, Deng-Ping and Shao, Ling},
    title     = {Specificity-Preserving RGB-D Saliency Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4681-4691}
}


Chris Dongjoo Kim, Jinseo Jeong, Sangwoo Moon, Gunhee Kim

Abstract: Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a replay-based continual learning framework that simultaneously addresses both catastrophic forgetting and noisy labels for the first time. Our solution is based on two observations; (i) forgetting can be mitigated even with noisy labels via self-supervised learning, and (ii) the purity of the replay buffer is crucial. Building on this regard, we propose two key components of our method: (i) a self-supervised replay technique named Self-Replay, which can circumvent erroneous training signals arising from noisy labeled data, and (ii) the Self-Centered filter that maintains a purified replay buffer via centrality-based stochastic graph ensembles. The empirical results on MNIST, CIFAR-10, CIFAR-100, and WebVision with real-world noise demonstrate that our framework can maintain a highly pure replay buffer amidst noisy streamed data while greatly outperforming the combinations of the state-of-the-art continual learning and noisy label learning methods.

@InProceedings{Kim_2021_ICCV,
    author    = {Kim, Chris Dongjoo and Jeong, Jinseo and Moon, Sangwoo and Kim, Gunhee},
    title     = {Continual Learning on Noisy Data Streams via Self-Purified Replay},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {537-547}
}


Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, Angjoo Kanazawa

Abstract: We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method can render 800x800 images at more than 150 FPS, which is over 3000 times faster than conventional NeRFs. We do so without sacrificing quality while preserving the ability of NeRFs to perform free-viewpoint rendering of scenes with arbitrary geometry and view-dependent effects. Real-time performance is achieved by pre-tabulating the NeRF into a PlenOctree. In order to preserve view-dependent effects such as specularities, we factorize the appearance via closed-form spherical basis functions. Specifically, we show that it is possible to train NeRFs to predict a spherical harmonic representation of radiance, removing the viewing direction as an input to the neural network. Furthermore, we show that PlenOctrees can be directly optimized to further minimize the reconstruction loss, which leads to equal or better quality compared to competing methods. Moreover, this octree optimization step can be used to reduce the training time, as we no longer need to wait for the NeRF training to converge fully. Our real-time neural rendering approach may potentially enable new applications such as 6-DOF industrial and product visualizations, as well as next generation AR/VR systems. PlenOctrees are amenable to in-browser rendering as well; please visit the project page for the interactive online demo, as well as video and code: https://alexyu.net/plenoctrees.

@InProceedings{Yu_2021_ICCV,
    author    = {Yu, Alex and Li, Ruilong and Tancik, Matthew and Li, Hao and Ng, Ren and Kanazawa, Angjoo},
    title     = {PlenOctrees for Real-Time Rendering of Neural Radiance Fields},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5752-5761}
}


Geonwoon Jang, Wooseok Lee, Sanghyun Son, Kyoung Mu Lee

Abstract: Learning-based image denoising methods have been bounded to situations where well-aligned noisy and clean images are given, or samples are synthesized from predetermined noise models, e.g., Gaussian. While recent generative noise modeling methods aim to simulate the unknown distribution of real-world noise, several limitations still exist. In a practical scenario, a noise generator should learn to simulate the general and complex noise distribution without using paired noisy and clean images. However, since existing methods are constructed on the unrealistic assumption of real-world noise, they tend to generate implausible patterns and cannot express complicated noise maps. Therefore, we introduce a Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using any paired examples. We construct the noise generator in C2N accordingly with each component of real-world noise characteristics to express a wide range of noise accurately. Combined with our C2N, conventional denoising CNNs can be trained to outperform existing unsupervised methods on challenging real-world benchmarks by a large margin.

@InProceedings{Jang_2021_ICCV,
    author    = {Jang, Geonwoon and Lee, Wooseok and Son, Sanghyun and Lee, Kyoung Mu},
    title     = {C2N: Practical Generative Noise Modeling for Real-World Denoising},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2350-2359}
}


Robin Chan, Matthias Rottmann, Hanno Gottschalk

Abstract: Deep neural networks (DNNs) for the semantic segmentation of images are usually trained to operate on a predefined closed set of object classes. This is in contrast to the ""open world"" setting where DNNs are envisioned to be deployed to. From a functional safety point of view, the ability to detect so-called ""out-of-distribution"" (OoD) samples, i.e., objects outside of a DNN's semantic space, is crucial for many applications such as automated driving. A natural baseline approach to OoD detection is to threshold on the pixel-wise softmax entropy. We present a two-step procedure that significantly improves that approach. Firstly, we utilize samples from the COCO dataset as OoD proxy and introduce a second training objective to maximize the softmax entropy on these samples. Starting from pretrained semantic segmentation networks we re-train a number of DNNs on different in-distribution datasets and consistently observe improved OoD detection performance when evaluating on completely disjoint OoD datasets. Secondly, we perform a transparent post-processing step to discard false positive OoD samples by so-called ""meta classification"". To this end, we apply linear models to a set of hand-crafted metrics derived from the DNN's softmax probabilities. In our experiments we consistently observe a clear additional gain in OoD detection performance, cutting down the number of detection errors by 52% when comparing the best baseline with our results. We achieve this improvement sacrificing only marginally in original segmentation performance. Therefore, our method contributes to safer DNNs with more reliable overall system performance.

@InProceedings{Chan_2021_ICCV,
    author    = {Chan, Robin and Rottmann, Matthias and Gottschalk, Hanno},
    title     = {Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5128-5137}
}


Lichen Zhao, Daigang Cai, Lu Sheng, Dong Xu

Abstract: Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features, but (1) how to model complex relations for generating context-aware object proposals and (2) how to leverage proposal relations to distinguish the true target object from similar proposals are not fully studied yet. Inspired by the well-known transformer architecture, we propose a relation-aware visual grounding method on 3D point clouds, named as 3DVG-Transformer, to fully utilize the contextual clues for relationenhanced proposal generation and cross-modal proposal disambiguation, which are enabled by a newly designed coordinate-guided contextual aggregation (CCA) module in the object proposal generation stage, and a multiplex attention (MA) module in the cross-modal feature fusion stage. We validate that our 3DVG-Transformer outperforms the state-of-the-art methods by a large margin, on two point cloud-based visual grounding datasets, ScanRefer and Nr3D/Sr3D from ReferIt3D, especially for complex scenarios containing multiple objects of the same category.

@InProceedings{Zhao_2021_ICCV,
    author    = {Zhao, Lichen and Cai, Daigang and Sheng, Lu and Xu, Dong},
    title     = {3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2928-2937}
}


Gregory Vaksman, Michael Elad, Peyman Milanfar

Abstract: The non-local self-similarity property of natural images has been exploited extensively for solving various image processing problems. When it comes to video sequences, harnessing this force is even more beneficial due to the temporal redundancy. In the context of image and video denoising, many classically-oriented algorithms employ self-similarity, splitting the data into overlapping patches, gathering groups of similar ones and processing these together somehow. With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. This work proposes a novel approach for leveraging self-similarity in the context of video denoising, while still relying on a regular convolutional architecture. We introduce a concept of patch-craft frames - artificial frames that are similar to the real ones, built by tiling matched patches. Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN. We demonstrate the substantial boost in denoising performance obtained with the proposed approach.

@InProceedings{Vaksman_2021_ICCV,
    author    = {Vaksman, Gregory and Elad, Michael and Milanfar, Peyman},
    title     = {Patch Craft: Video Denoising by Deep Modeling and Patch Matching},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2157-2166}
}


Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, Brian Lim

Abstract: The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy. Explainable artificial intelligence (XAI) provides more information to help users to understand model decisions, yet this additional knowledge exposes additional risks for privacy attacks. Hence, providing explanation harms privacy. We study this risk for image-based model inversion attacks and identified several attack architectures with increasing performance to reconstruct private image data from model explanations. We have developed several multi-modal transposed CNN architectures that achieve significantly higher inversion performance than using the target model prediction only. These XAI-aware inversion models were designed to exploit the spatial knowledge in image explanations. To understand which explanations have higher privacy risk, we analyzed how various explanation types and factors influence inversion performance. In spite of some models not providing explanations, we further demonstrate increased inversion performance even for non-explainable target models by exploiting explanations of surrogate models through attention transfer. This method first inverts an explanation from the target prediction, then reconstructs the target image. These threats highlight the urgent and significant privacy risks of explanations and calls attention for new privacy preservation techniques that balance the dual-requirement for AI explainability and privacy.

@InProceedings{Zhao_2021_ICCV,
    author    = {Zhao, Xuejun and Zhang, Wencan and Xiao, Xiaokui and Lim, Brian},
    title     = {Exploiting Explanations for Model Inversion Attacks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {682-692}
}


Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query

Guanyu Cai, Jun Zhang, Xinyang Jiang, Yifei Gong, Lianghua He, Fufu Yu, Pai Peng, Xiaowei Guo, Feiyue Huang, Xing Sun

Abstract: Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its influence on text-based image retrieval. Previous interactive methods tackle the problem by passively receiving users' feedback to supplement the incomplete query iteratively, which is time-consuming and requires heavy user effort. Instead, we propose a novel retrieval framework that conducts the interactive process in an Ask-and-Confirm fashion, where AI actively searches for discriminative details missing in the current query, and users only need to confirm AI's proposal. Specifically, we propose an object-based interaction to make the interactive retrieval more user-friendly and present a reinforcement-learning-based policy to search for discriminative objects. Furthermore, since fully-supervised training is often infeasible due to the difficulty of obtaining human-machine dialog data, we present a weakly-supervised training strategy that needs no human-annotated dialogs other than a text-image dataset. Experiments show that our framework significantly improves the performance of text-based image retrieval. Code is available at https://github.com/CuthbertCai/Ask-Confirm.

@InProceedings{Cai_2021_ICCV,
    author    = {Cai, Guanyu and Zhang, Jun and Jiang, Xinyang and Gong, Yifei and He, Lianghua and Yu, Fufu and Peng, Pai and Guo, Xiaowei and Huang, Feiyue and Sun, Xing},
    title     = {Ask\&Confirm: Active Detail Enriching for Cross-Modal Retrieval With Partial Query},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1835-1844}
}


Avi Ben-Cohen, Nadav Zamir, Emanuel Ben-Baruch, Itamar Friedman, Lihi Zelnik-Manor

Abstract: Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as image retrieval of natural images. We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately. This study introduces an end-to-end model training for multi-label zero-shot learning that supports the semantic diversity of the images and labels. We propose to use an embedding matrix having principal embedding vectors trained using a tailored loss function. In addition, during training, we suggest up-weighting in the loss function image samples presenting higher semantic diversity to encourage the diversity of the embedding matrix. Extensive experiments show that our proposed method improves the zero-shot model's quality in tag-based image retrieval achieving SoTA results on several common datasets (NUS-Wide, COCO, Open Images).

@InProceedings{Ben-Cohen_2021_ICCV,
    author    = {Ben-Cohen, Avi and Zamir, Nadav and Ben-Baruch, Emanuel and Friedman, Itamar and Zelnik-Manor, Lihi},
    title     = {Semantic Diversity Learning for Zero-Shot Multi-Label Classification},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {640-650}
}


Yue Qiu, Shintaro Yamamoto, Kodai Nakashima, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, Yutaka Satoh

Abstract: Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-change captioning dataset; (ii) We benchmark existing state-of-the-art methods of single change captioning on multi-change captioning; (iii) We further propose Multi-Change Captioning transformers (MCCFormers) that identify change regions by densely correlating different regions in image pairs and dynamically determines the related change regions with words in sentences. The proposed method obtained the highest scores on four conventional change captioning evaluation metrics for multi-change captioning. Additionally, our proposed method can separate attention maps for each change and performs well with respect to change localization. Moreover, the proposed framework outperformed the previous state-of-the-art methods on an existing change captioning benchmark, CLEVR-Change, by a large margin (+6.1 on BLEU-4 and +9.7 on CIDEr scores), indicating its general ability in change captioning tasks. The code and dataset are available at the project page.

@InProceedings{Qiu_2021_ICCV,
    author    = {Qiu, Yue and Yamamoto, Shintaro and Nakashima, Kodai and Suzuki, Ryota and Iwata, Kenji and Kataoka, Hirokatsu and Satoh, Yutaka},
    title     = {Describing and Localizing Multiple Changes With Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1971-1980}
}


Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao

Abstract: Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network useful for many dense prediction tasks. Unlike the recently-proposed Vision Transformer (ViT) that was designed for image classification specifically, we introduce the Pyramid Vision Transformer (PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to current state of the arts. (1) Different from ViT that typically yields low-resolution outputs and incurs high computational and memory costs, PVT not only can be trained on dense partitions of an image to achieve high output resolution, which is important for dense prediction, but also uses a progressive shrinking pyramid to reduce the computations of large feature maps. (2) PVT inherits the advantages of both CNN and Transformer, making it a unified backbone for various vision tasks without convolutions, where it can be used as a direct replacement for CNN backbones. (3) We validate PVT through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation. For example, with a comparable number of parameters, PVT+RetinaNet achieves 40.4 AP on the COCO dataset, surpassing ResNet50+RetinNet (36.3 AP) by 4.1 absolute AP. We hope that PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future research.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
    title     = {Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {568-578}
}


Xiaotian Qiao, Gerhard P. Hancke, Rynson W.H. Lau

Abstract: Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined intensity and geometry priors of flares, and may fail to distinguish the difference between light sources and flare artifacts. We observe that the conditions of the light source in the image play an important role in the resulting flares. In this paper, we present a deep framework with light source aware guidance for single-image flare removal (SIFR). In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance. By learning the underlying relationships between the two types of regions, our approach can remove different kinds of flares from the image. In addition, instead of using paired training data which are difficult to collect, we propose the first unpaired flare removal dataset and new cycle-consistency constraints to obtain more diverse examples and avoid manual annotations. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively. We also show that our model can be applied to flare effect manipulation (e.g., adding or changing image flares).

@InProceedings{Qiao_2021_ICCV,
    author    = {Qiao, Xiaotian and Hancke, Gerhard P. and Lau, Rynson W.H.},
    title     = {Light Source Guided Single-Image Flare Removal From Unpaired Data},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4177-4185}
}


Score-Based Point Cloud Denoising

Abstract: Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples p(x) convolved with some noise model n, leading to (p * n)(x) whose mode is the underlying clean surface. To denoise a noisy point cloud, we propose to increase the log-likelihood of each point from p * n via gradient ascent---iteratively updating each point's position. Since p * n is unknown at test-time, and we only need the score (i.e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of p * n given only noisy point clouds as input. We derive objective functions for training the network and develop a denoising algorithm leveraging on the estimated scores. Experiments demonstrate that the proposed model outperforms state-of-the-art methods under a variety of noise models, and shows the potential to be applied in other tasks such as point cloud upsampling.

@InProceedings{Luo_2021_ICCV,
    author    = {Luo, Shitong and Hu, Wei},
    title     = {Score-Based Point Cloud Denoising},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4583-4592}
}


Vivien Sainte Fare Garnot, Loic Landrieu

Abstract: Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self-attention to extract rich and adaptive multi-scale spatio-temporal features. We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing network architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and the PASTIS dataset are publicly available at (link-upon-publication).

@InProceedings{Garnot_2021_ICCV,
    author    = {Garnot, Vivien Sainte Fare and Landrieu, Loic},
    title     = {Panoptic Segmentation of Satellite Image Time Series With Convolutional Temporal Attention Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4872-4881}
}


Qixing Huang, Xiangru Huang, Bo Sun, Zaiwei Zhang, Junfeng Jiang, Chandrajit Bajaj

Abstract: This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on a local approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the unsupervised loss via a spectral decomposition of the Hessian of the ARAP loss. Our loss nicely decouples pose and shape variations through a robust norm. The loss admits simple closed-form expressions. It is easy to train and can be plugged into any standard generation models, e.g., VAE and GAN. Experimental results show that our approach outperforms existing shape generation approaches considerably across various datasets such as DFAUST, Animal, and Bone.

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Qixing and Huang, Xiangru and Sun, Bo and Zhang, Zaiwei and Jiang, Junfeng and Bajaj, Chandrajit},
    title     = {ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5815-5825}
}


Dominik Rivoir, Micha Pfeiffer, Reuben Docea, Fiona Kolbinger, Carina Riediger, Jürgen Weitz, Stefanie Speidel

Abstract: Research in unpaired video translation has mainly focused on short-term temporal consistency by conditioning on neighboring frames. However for transfer from simulated to photorealistic sequences, available information on the underlying geometry offers potential for achieving global consistency across views. We propose a novel approach which combines unpaired image translation with neural rendering to transfer simulated to photorealistic surgical abdominal scenes. By introducing global learnable textures and a lighting-invariant view-consistency loss, our method produces consistent translations of arbitrary views and thus enables long-term consistent video synthesis. We design and test our model to generate video sequences from minimally-invasive surgical abdominal scenes. Because labeled data is often limited in this domain, photorealistic data where ground truth information from the simulated domain is preserved is especially relevant. By extending existing image-based methods to view-consistent videos, we aim to impact the applicability of simulated training and evaluation environments for surgical applications. Code and data: http://opencas.dkfz.de/video-sim2real.

@InProceedings{Rivoir_2021_ICCV,
    author    = {Rivoir, Dominik and Pfeiffer, Micha and Docea, Reuben and Kolbinger, Fiona and Riediger, Carina and Weitz, J\"urgen and Speidel, Stefanie},
    title     = {Long-Term Temporally Consistent Unpaired Video Translation From Simulated Surgical 3D Data},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3343-3353}
}


Jinheng Xie, Cheng Luo, Xiangping Zhu, Ziqi Jin, Weizeng Lu, Linlin Shen

Abstract: We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objects using the low-level feature based activation maps. In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner. In the second stage, we employ an evaluator to evaluate the activation maps predicted by the activation map generator. Based on this, we further propose a weighted entropy loss, an attentive erasing, and an area loss to drive the activation map generator to substantially reduce the uncertainty of activations between object and background, and explore less discriminative regions. Based on the low-level object information preserved in the first stage, the second stage model gradually generates a well-separated, complete, and compact activation map of object in the image, which can be easily thresholded for accurate localization. Extensive experiments on CUB-200-2011 and ImageNet-1K datasets show that our framework surpasses previous methods by a large margin, which sets a new state-of-the-art for WSOL. Code will be available soon.

@InProceedings{Xie_2021_ICCV,
    author    = {Xie, Jinheng and Luo, Cheng and Zhu, Xiangping and Jin, Ziqi and Lu, Weizeng and Shen, Linlin},
    title     = {Online Refinement of Low-Level Feature Based Activation Map for Weakly Supervised Object Localization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {132-141}
}


Huanyu Wang, Songyuan Li, Shihao Su, Zequn Qin, Xi Li

Abstract: Dynamic inference networks, aimed at promoting computational efficiency, go along an adaptive executing path for a given sample. Prevalent methods typically assign a router for each convolutional block and sequentially make block-by-block executing decisions, without considering the relations during the dynamic inference. In this paper, we model the relations for dynamic inference from two aspects: the routers and the samples. We design a novel type of router called the relational router to model the relations among routers for a given sample. In principle, the current relational router aggregates the contextual features of preceding routers by graph convolution and propagates its router features to subsequent ones, making the executing decision for the current block in a long-range manner. Furthermore, we model the relation between samples by introducing a Sample Relation Module (SRM), encouraging correlated samples to go along correlated executing paths. As a whole, we call our method the Relational Dynamic Inference Network (RDI-Net). Extensive experiments on CIFAR-10/100 and ImageNet show that RDI-Net achieves state-of-the-art performance and computational cost reduction. Our code and models will be made publicly available.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Huanyu and Li, Songyuan and Su, Shihao and Qin, Zequn and Li, Xi},
    title     = {RDI-Net: Relational Dynamic Inference Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4621-4630}
}


Yunsong Zhou, Hongzi Zhu, Chunqin Li, Tiankai Cui, Shan Chang, Minyi Guo

Abstract: Online semantic segmentation on a time series of point cloud frames is an essential task in autonomous driving. Existing models focus on single-frame segmentation, which cannot achieve satisfactory segmentation accuracy and offer unstably flicker among frames. In this paper, we propose a light-weight semantic segmentation framework for large-scale point cloud series, called TempNet, which can improve both the accuracy and the stability of existing semantic segmentation models by combining a novel frame aggregation scheme. To be computational cost efficient, feature extraction and aggregation are only conducted on a small portion of key frames via a temporal feature aggregation (TFA) network using an attentional pooling mechanism, and such enhanced features are propagated to the intermediate non-key frames. To avoid information loss from non-key frames, a partial feature update (PFU) network is designed to partially update the propagated features with the local features extracted on a non-key frame if a large disparity between the two is quickly assessed. As a result, consistent and information-rich features can be obtained for each frame. We implement TempNet on five state-of-the-art (SOTA) point cloud segmentation models and conduct extensive experiments on the SemanticKITTI dataset. Results demonstrate that TempNet outperforms SOTA competitors by wide margins with little extra computational cost.

@InProceedings{Zhou_2021_ICCV,
    author    = {Zhou, Yunsong and Zhu, Hongzi and Li, Chunqin and Cui, Tiankai and Chang, Shan and Guo, Minyi},
    title     = {TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7118-7127}
}


Qinghao Ye, Xiyue Shen, Yuan Gao, Zirui Wang, Qi Bi, Ping Li, Guang Yang

Abstract: Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i.e., where the highlight moments are in long videos) for supervised learning. In this paper, we propose a novel weakly supervised method that can learn to detect highlights by mining video characteristics with video level annotations (topic tags) only. Particularly, we exploit audio-visual features to enhance video representation and take temporal cues into account for improving detection performance. Our contributions are threefold: 1) we propose an audio-visual tensor fusion mechanism that efficiently models the complex association between two modalities while reducing the gap of the heterogeneity between the two modalities; 2) we introduce a novel hierarchical temporal context encoder to embed local temporal clues in between neighboring segments; 3) finally, we alleviate the gradient vanishing problem theoretically during model optimization with attention-gated instance aggregation. Extensive experiments on two benchmark datasets (YouTube Highlights and TVSum) have demonstrated our method outperforms other state-of-the-art methods with remarkable improvements.

@InProceedings{Ye_2021_ICCV,
    author    = {Ye, Qinghao and Shen, Xiyue and Gao, Yuan and Wang, Zirui and Bi, Qi and Li, Ping and Yang, Guang},
    title     = {Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7950-7959}
}


Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, Shih-Fu Chang

Abstract: Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. This field sees recent improvement owing to the meta-learning techniques by learning how to match between the query image and few-shot class examples, such that the learned model can generalize to few-shot novel classes. However, currently, most of the meta-learning-based methods perform parwise matching between query image regions (usually proposals) and novel classes separately, therefore failing to take into account multiple relationships among them. In this paper, we propose a novel FSOD model using heterogeneous graph convolutional networks. Through efficient message passing among all the proposal and class nodes with three different types of edges, we could obtain context-aware proposal features and query-adaptive, multiclass-enhanced prototype representations for each class, which could help promote the pairwise matching and improve final FSOD accuracy. Extensive experimental results show that our proposed model, denoted as QA-FewDet, outperforms the current state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under different shots and evaluation metrics.

@InProceedings{Han_2021_ICCV,
    author    = {Han, Guangxing and He, Yicheng and Huang, Shiyuan and Ma, Jiawei and Chang, Shih-Fu},
    title     = {Query Adaptive Few-Shot Object Detection With Heterogeneous Graph Convolutional Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3263-3272}
}


Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh, Varun Jampani, R. Venkatesh Babu

Abstract: Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation. However, almost all prior arts assume concurrent access to both labeled source and unlabeled target, making them unsuitable for scenarios demanding source-free adaptation. In this work, we enable source-free DA by partitioning the task into two: a) source-only domain generalization and b) source-free target adaptation. Towards the former, we provide theoretical insights to develop a multi-head framework trained with a virtually extended multi-source dataset, aiming to balance generalization and specificity. Towards the latter, we utilize the multi-head framework to extract reliable target pseudo-labels for self-training. Additionally, we introduce a novel conditional prior-enforcing auto-encoder that discourages spatial irregularities, thereby enhancing the pseudo-label quality. Experiments on the standard GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes benchmarks show our superiority even against the non-source-free prior-arts. Further, we show our compatibility with online adaptation enabling deployment in a sequentially changing environment.

@InProceedings{Kundu_2021_ICCV,
    author    = {Kundu, Jogendra Nath and Kulkarni, Akshay and Singh, Amit and Jampani, Varun and Babu, R. Venkatesh},
    title     = {Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7046-7056}
}


Xiaohan Ding, Tianxiang Hao, Jianchao Tan, Ji Liu, Jungong Han, Yuchen Guo, Guiguang Ding

Abstract: We propose ResRep, a novel method for lossless channel pruning (a.k.a. filter pruning), which slims down a CNN by reducing the width (number of output channels) of convolutional layers. Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into the remembering parts and forgetting parts, where the former learn to maintain the performance and the latter learn to prune. Via training with regular SGD on the former but a novel update rule with penalty gradients on the latter, we realize structured sparsity. Then we equivalently merge the remembering and forgetting parts into the original architecture with narrower layers. In this sense, ResRep can be viewed as a successful application of Structural Re-parameterization. Such a methodology distinguishes ResRep from the traditional learning-based pruning paradigm that applies a penalty on parameters to produce sparsity, which may suppress the parameters essential for the remembering. ResRep slims down a standard ResNet-50 with 76.15% accuracy on ImageNet to a narrower one with only 45% FLOPs and no accuracy drop, which is the first to achieve lossless pruning with such a high compression ratio. The code and models are at https://github.com/DingXiaoH/ResRep.

@InProceedings{Ding_2021_ICCV,
    author    = {Ding, Xiaohan and Hao, Tianxiang and Tan, Jianchao and Liu, Ji and Han, Jungong and Guo, Yuchen and Ding, Guiguang},
    title     = {ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4510-4520}
}


Zhen Zhong, Guobao Xiao, Linxin Zheng, Yan Lu, Jiayi Ma

Abstract: We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: "-" structure and "|" structure. "-" structure adopts an iterative strategy to learn correspondence features. "|" structure integrates all the features of the iterations and outputs the correspondence weight. In addition, we introduce Permutation-Equivariant Context Squeeze-and-Excitation module, an adapted version of SE module, to process sparse correspondences in a permutation-equivariant way and capture both global and channel-wise contextual information. Extensive experiments on outdoor and indoor scenes show that the proposed T-Net achieves state-of-the-art performance. On outdoor scenes (YFCC100M dataset), T-Net achieves an mAP of 52.28%, a 34.22% precision increase from the best-published result (38.95%). On indoor scenes (SUN3D dataset), T-Net (19.71%) obtains a 21.82% precision increase from the best-published result (16.18%).

@InProceedings{Zhong_2021_ICCV,
    author    = {Zhong, Zhen and Xiao, Guobao and Zheng, Linxin and Lu, Yan and Ma, Jiayi},
    title     = {T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1950-1959}
}


Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, Weidong Cai

Abstract: Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds. Sequences of connected points (curves) are initially grouped by taking guided walks in the point clouds, and then subsequently aggregated back to augment their point-wise features. We provide an effective implementation of the proposed aggregation strategy including a novel curve grouping operator followed by a curve aggregation operator. Our method was benchmarked on several point cloud analysis tasks where we achieved the state-of-the-art classification accuracy of 94.2% on the ModelNet40 classification task, instance IoU of 86.8% on the ShapeNetPart segmentation task and cosine error of 0.11 on the ModelNet40 normal estimation task.

@InProceedings{Xiang_2021_ICCV,
    author    = {Xiang, Tiange and Zhang, Chaoyi and Song, Yang and Yu, Jianhui and Cai, Weidong},
    title     = {Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {915-924}
}


Abstract: Point cloud registration is the process of using the common structures in two point clouds to splice them together. To find out these common structures and make these structures match more accurately, we investigate the direction of interacting information of the source and target point clouds. To this end, we propose a Feature Interactive Representation learning Network (FIRE-Net), which can explore feature interaction among the source and target point clouds from different levels. Specifically, we first introduce a Combined Feature Encoder (CFE) based on feature interaction intra point cloud. CFE extracts interactive features intra each point cloud and combines them to enhance the ability of the network to describe the local geometric structure. Then, we propose a feature interaction mechanism inter point clouds which includes a Local Interaction Unit (LIU) and a Global Interaction Unit (GIU). The former is used to interact information between point pairs across two point clouds, thus the point features in one point cloud and its similar point features in another point cloud can be aware of each other. The latter is applied to change the per-point features depending on the global cross information of two point clouds, thus one point cloud has the global perception of another. Extensive experiments on partially overlapping point cloud registration show that our method achieves state-of-the-art performance.

@InProceedings{Wu_2021_ICCV,
    author    = {Wu, Bingli and Ma, Jie and Chen, Gaojie and An, Pei},
    title     = {Feature Interactive Representation for Point Cloud Registration},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5530-5539}
}


Linjie Li, Jie Lei, Zhe Gan, Jingjing Liu

Abstract: Benefiting from large-scale pre-training, we have witnessed significant performance boost on the popular Visual Question Answering (VQA) task. Despite rapid progress, it remains unclear whether these state-of-the-art (SOTA) models are robust when encountering examples in the wild. To study this, we introduce Adversarial VQA, a new large-scale VQA benchmark, collected iteratively via an adversarial human-and-model-in-the-loop procedure. Through this new benchmark, we discover several interesting findings. (i) Surprisingly, we find that during dataset collection, non-expert annotators can easily attack SOTA VQA models successfully. (ii) Both large-scale pre-trained models and adversarial training methods achieve far worse performance on the new benchmark than over standard VQA v2 dataset, revealing the fragility of these models while demonstrating the effectiveness of our adversarial dataset. (iii) When used for data augmentation, our dataset can effectively boost model performance on other robust VQA benchmarks. We hope our Adversarial VQA dataset can shed new light on robustness study in the community and serve as a valuable benchmark for future work.

@InProceedings{Li_2021_ICCV,
    author    = {Li, Linjie and Lei, Jie and Gan, Zhe and Liu, Jingjing},
    title     = {Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2042-2051}
}


Jiabo Huang, Yang Liu, Shaogang Gong, Hailin Jin

Abstract: Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos. For supervised model training, a temporal annotation of both the start and end time index of each video segment for a sentence (a video moment) must be given. This is not only very expensive but also sensitive to ambiguity and subjective annotation bias, a much harder task than image labelling. In this work, we develop a more accurate weakly-supervised solution by introducing Cross-Sentence Relations Mining (CRM) in video moment proposal generation and matching when only a paragraph description of activities without per-sentence temporal annotation is available. Specifically, we explore two cross-sentence relational constraints: (1) Temporal ordering and (2) semantic consistency among sentences in a paragraph description of video activities. Existing weakly-supervised techniques only consider within-sentence video segment correlations in training without considering cross-sentence paragraph context. This can mislead due to ambiguous expressions of individual sentences with visually indiscriminate video moment proposals in isolation. Experiments on two publicly available activity localisation datasets show the advantages of our approach over the state-of-the-art weakly supervised methods, especially so when the video activity descriptions become more complex.

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Jiabo and Liu, Yang and Gong, Shaogang and Jin, Hailin},
    title     = {Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7199-7208}
}


Harsh Rangwani, Arihant Jain, Sumukh K Aithal, R. Venkatesh Babu

Abstract: Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario's it might be feasible to get labels for a small proportion of target data. In these scenarios, it is important to select maximally-informative samples to label and find an effective way to combine them with the existing knowledge from source data. Towards achieving this, we propose S^3VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure through novel improvements to effectively utilize all the available data for improving generalization on target. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts. The project page with additional details is available here: https://sites.google.com/iisc.ac.in/s3vaada-iccv2021.

@InProceedings{Rangwani_2021_ICCV,
    author    = {Rangwani, Harsh and Jain, Arihant and Aithal, Sumukh K and Babu, R. Venkatesh},
    title     = {S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7516-7525}
}


Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Zhengrong Zuo, Changxin Gao, Nong Sang

Abstract: Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism and gradient vanishing, hence it is hard to be optimized. In this paper, we propose a new encoder-decoder framework based on Transformers, named OadTR, to tackle these problems. The encoder attached with a task token aims to capture the relationships and global interactions between historical observations. The decoder extracts auxiliary information by aggregating anticipated future clip representations. Therefore, OadTR can recognize current actions by encoding historical information and predicting future context simultaneously. We extensively evaluate the proposed OadTR on three challenging datasets: HDD, TVSeries, and THUMOS14. The experimental results show that OadTR achieves higher training and inference speeds than current RNN based approaches, and significantly outperforms the state-of-the-art methods in terms of both mAP and mcAP. Code is available at https: //github.com/wangxiang1230/OadTR.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Xiang and Zhang, Shiwei and Qing, Zhiwu and Shao, Yuanjie and Zuo, Zhengrong and Gao, Changxin and Sang, Nong},
    title     = {OadTR: Online Action Detection With Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7565-7575}
}


Yuankai Qi, Zizheng Pan, Yicong Hong, Ming-Hsuan Yang, Anton van den Hengel, Qi Wu

Abstract: Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this requires a model to match different nouns (e.g., TV, table) against the same input view feature. In this work, we propose an object-informed sequential BERT to encode visual perceptions and linguistic instructions at the same fine-grained level, namely objects and words. Our sequential BERT also enables the visual-textual clues to be interpreted in light of the temporal context, which is crucial to multi-round VLN tasks. Additionally, we enable the model to identify the relative direction (e.g., left/right/front/back) of each navigable location and the room type (e.g., bedroom, kitchen) of its current and final navigation goal, as such information is widely mentioned in instructions implying the desired next and final locations. We thus enable the model to know-where the objects lie in the images, and to know-where they stand in the scene. Extensive experiments demonstrate the effectiveness compared against several state-of-the-art methods on three indoor VLN tasks: REVERIE, NDH, and R2R. Project repository: https://github.com/YuankaiQi/ORIST

@InProceedings{Qi_2021_ICCV,
    author    = {Qi, Yuankai and Pan, Zizheng and Hong, Yicong and Yang, Ming-Hsuan and van den Hengel, Anton and Wu, Qi},
    title     = {The Road To Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1655-1664}
}


Xumin Yu, Yongming Rao, Wenliang Zhao, Jiwen Lu, Jie Zhou

Abstract: Assessing action quality is challenging due to the subtle differences between videos and large variations in scores. Most existing approaches tackle this problem by regressing a quality score from a single video, suffering a lot from the large inter-video score variations. In this paper, we show that the relations among videos can provide important clues for more accurate action quality assessment during both training and inference. Specifically, we reformulate the problem of action quality assessment as regressing the relative scores with reference to another video that has shared attributes (e.g. category and difficulty), instead of learning unreferenced scores. Following this formulation, we propose a new contrastive regression (CoRe) framework to learn the relative scores by pair-wise comparison, which highlights the differences between videos and guides the models to learn the key hints for assessment. In order to further exploit the relative information between two videos, we devise a group-aware regression tree to convert the conventional score regression into two easier sub-problems: coarse-to-fine classification and regression in small intervals. To demonstrate the effectiveness of CoRe, we conduct extensive experiments on three mainstream AQA datasets including AQA-7, MTL-AQA, and JIGSAWS. Our approaches outperform previous methods by a large margin and establish new state-of-the-art on all three benchmarks.

@InProceedings{Yu_2021_ICCV,
    author    = {Yu, Xumin and Rao, Yongming and Zhao, Wenliang and Lu, Jiwen and Zhou, Jie},
    title     = {Group-Aware Contrastive Regression for Action Quality Assessment},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7919-7928}
}


Ziyi Meng, Zhenming Yu, Kun Xu, Xin Yuan

Abstract: We consider using untrained neural networks to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Various SCI systems have been built in recent years to capture data such as high-speed videos, hyperspectral images, and the state-of-the-art reconstruction is obtained by the deep neural networks. However, most of these networks are trained in an end-to-end manner by a large amount of corpus with sometimes simulated ground truth, measurement pairs. In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction. Extensive synthetic and real data results show that the proposed algorithm without training is capable of achieving competitive results to the training based networks. Furthermore, by integrating the proposed method with a pre-trained deep denoising prior, we have achieved higher performance than existing state-of-the-art.

@InProceedings{Meng_2021_ICCV,
    author    = {Meng, Ziyi and Yu, Zhenming and Xu, Kun and Yuan, Xin},
    title     = {Self-Supervised Neural Networks for Spectral Snapshot Compressive Imaging},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2622-2631}
}


Haobo Jiang, Yaqi Shen, Jin Xie, Jun Li, Jianjun Qian, Jian Yang

Abstract: In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, given a pair of point clouds, the sampling network learns a prior sampling distribution over the transformation space. The learned sampling distribution can be used as a "good"" initialization of the differentiable CEM module. In our differentiable CEM module, we first propose a maximum consensus criterion based alignment metric as the reward function for the point cloud registration task. Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations. Particularly, the future rewards of the sampled transforms are obtained by performing the iterative closest point (ICP) algorithm on the transformed state. By selecting the top-k transformations with the highest scores, we iteratively update the sampling distribution. Furthermore, in order to make the CEM differentiable, we use the sparsemax function to replace the hard top-k selection. Finally, we formulate a Geman-McClure estimator based loss to train our end-to-end registration model. Extensive experimental results demonstrate the good registration performance of our method on benchmark datasets.

@InProceedings{Jiang_2021_ICCV,
    author    = {Jiang, Haobo and Shen, Yaqi and Xie, Jin and Li, Jun and Qian, Jianjun and Yang, Jian},
    title     = {Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6128-6137}
}


Zunlei Feng, Zhonghua Wang, Xinchao Wang, Yining Mao, Thomas Li, Jie Lei, Yuexuan Wang, Mingli Song

Abstract: Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What's more, accurate segmentation detection of nuclei can be challenging due to the large appearance variation, conjoined and overlapping nuclei, and serious degeneration of histological structures. Supervised methods highly rely on massive annotated samples. The existing two unsupervised methods are prone to failure on degenerated samples. This paper proposes a Mutual-Complementing Framework (MCF) for nuclei detection and segmentation in pathology images. Two branches of MCF are trained in the mutual-complementing manner, where the detection branch complements the pseudo mask of the segmentation branch, while the progressive trained segmentation branch complements the missing nucleus templates through calculating the mask residual between the predicted mask and detected result. In the detection branch, two response map fusion strategies and gradient direction based postprocessing are devised to obtain the optimal detection response. Furthermore, the confidence loss combined with the synthetic samples and self-finetuning is adopted to train the segmentation network with only high confidence areas. Extensive experiments demonstrate that MCF achieves comparable performance with only a few nucleus patches as supervision. Especially, MCF possesses good robustness (only dropping by about 6%) on degenerated samples, which are critical and common cases in clinical diagnosis.

@InProceedings{Feng_2021_ICCV,
    author    = {Feng, Zunlei and Wang, Zhonghua and Wang, Xinchao and Mao, Yining and Li, Thomas and Lei, Jie and Wang, Yuexuan and Song, Mingli},
    title     = {Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4036-4045}
}


Abstract: While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, e.g., the "hat" attribute and the "boots" attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the "hat" attribute should be consistent, whether it is a baseball cap, beret, or helmet. To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute. Specifically, we first propose a spatial consistency regularization to focus on reliable and stable attribute-related regions. Based on the precise attribute locations, we further propose a semantic consistency regularization to extract intrinsic and discriminative semantic features. We conduct extensive experiments on popular benchmarks including PA100K, RAP, and PETA. Results show that the proposed method performs favorably against state-of-the-art methods without increasing parameters.

@InProceedings{Jia_2021_ICCV,
    author    = {Jia, Jian and Chen, Xiaotang and Huang, Kaiqi},
    title     = {Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {962-971}
}


Mariella Dimiccoli, Lluís Garrido, Guillem Rodriguez-Corominas, Herwig Wendt

Abstract: Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and currently they represent the state of the art in HMS. Bucking this trend, in this paper, we propose a novel unsupervised model that learns a representation of the data and digs clustering information from the data itself. Our model is reminiscent of temporal subspace clustering, but presents two critical differences. First, we learn an auxiliary data matrix that can deviate from the initial data, hence confers more degrees of freedom to the coding matrix. Second, we introduce a regularization term for this auxiliary data matrix that preserves the local geometrical structure present in the high-dimensional space. The proposed model is efficiently optimized by using an original Alternating Direction Method of Multipliers (ADMM) formulation allowing to learn jointly the auxiliary data representation, a nonnegative dictionary and a coding matrix. Experimental results on four benchmark datasets for HMS demonstrate that our approach achieves significantly better clustering performance then state-of-the-art methods, including both unsupervised and more recent semi-supervised transfer learning approaches.

@InProceedings{Dimiccoli_2021_ICCV,
    author    = {Dimiccoli, Mariella and Garrido, Llu{\'\i}s and Rodriguez-Corominas, Guillem and Wendt, Herwig},
    title     = {Graph Constrained Data Representation Learning for Human Motion Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1460-1469}
}


Abstract: Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel categories on the fly with minimum online efforts. This is an object detection on demand (ODOD) task. It is challenging, because it is impossible to annotate large data on the fly, and the embedded systems are usually unable to perform back-propagation which is essential for training. Most existing few-shot detection methods are confronted here as they need extra training. We propose a novel morphable detector (MD), that simply "morphs"" some of its changeable parameters online estimated from the few samples, so as to detect novel categories without any extra training. The MD has two sets of parameters, one for the feature embedding and the other for category representation(called "prototypes""). Each category is associated with a hidden prototype to be learned by integrating the visual and semantic embeddings. The learning of the MD is based on the alternate learning of the feature embedding and the prototypes in an EM-like approach which allows the recovery of an unknown prototype from a few samples of a novel category. Once an MD is learned, it is able to use a few samples of a novel category to directly compute its prototype to fulfill the online morphing process. We have shown the superiority of the MD in Pascal, COCO and FSOD datasets.

@InProceedings{Zhao_2021_ICCV,
    author    = {Zhao, Xiangyun and Zou, Xu and Wu, Ying},
    title     = {Morphable Detector for Object Detection on Demand},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4771-4780}
}


Liangchen Song, Jialian Wu, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan

Abstract: Multi-view pedestrian detection aims to predict a bird's eye view (BEV) occupancy map from multiple camera views. This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views. In this paper, we propose a novel Stacked HOmography Transformations (SHOT) approach, which is motivated by approximating projections in 3D world coordinates via a stack of homographies. We first construct a stack of transformations for projecting views to the ground plane at different height levels. Then we design a soft selection module so that the network learns to predict the likelihood of the stack of transformations. Moreover, we provide an in-depth theoretical analysis on constructing SHOT and how well SHOT approximates projections in 3D world coordinates. SHOT is empirically verified to be capable of estimating accurate correspondences from individual views to the BEV map, leading to new state-of-the-art performance on standard evaluation benchmarks.

@InProceedings{Song_2021_ICCV,
    author    = {Song, Liangchen and Wu, Jialian and Yang, Ming and Zhang, Qian and Li, Yuan and Yuan, Junsong},
    title     = {Stacked Homography Transformations for Multi-View Pedestrian Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6049-6057}
}


Difei Gao, Ruiping Wang, Ziyi Bai, Xilin Chen

Abstract: Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects' state changes, which are still challenging for current AI systems. To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. Env-QA contains 23K egocentric videos, where each video is composed of a series of events about exploring and interacting in the environment. It also provides 85K questions to evaluate the ability of understanding the composition, layout, and state changes of the environment presented by the events in videos. Moreover, we propose a video QA model, Temporal Segmentation and Event Attention network (TSEA), which introduces event-level video representation and corresponding attention mechanisms to better extract environment information and answer questions. Comprehensive experiments demonstrate the effectiveness of our framework and show the formidable challenges of Env-QA in terms of long-term state tracking, multi-event temporal reasoning and event counting, etc.

@InProceedings{Gao_2021_ICCV,
    author    = {Gao, Difei and Wang, Ruiping and Bai, Ziyi and Chen, Xilin},
    title     = {Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1675-1685}
}


Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, Stephen Gould

Abstract: We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reasoning in rich image and language contexts. To address this issue, we collect the Compose Image Retrieval on Real-life images (CIRR) dataset, which consists of over 36,000 pairs of crowd-sourced, open-domain images with human-generated modifying text. To extend current methods to the open-domain, we propose CIRPLANT, a transformer based model that leverages rich pre-trained vision-and-language (V&L) knowledge for modifying visual features conditioned on natural language. Retrieval is then done by nearest neighbor lookup on the modified features. We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion. Together with the release of CIRR, we believe this work will inspire further research on composed image retrieval. Our dataset, code and pre-trained models are available at https://cuberick-orion.github.io/CIRR/.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Zheyuan and Rodriguez-Opazo, Cristian and Teney, Damien and Gould, Stephen},
    title     = {Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2125-2134}
}


Chengjian Feng, Yujie Zhong, Weilin Huang

Abstract: The conventional detectors tend to make imbalanced classification and suffer performance drop, when the distribution of the training data is severely skewed. In this paper, we propose to use the mean classification score to indicate the classification accuracy for each category during training. Based on this indicator, we balance the classification via an Equilibrium Loss (EBL) and a Memory-augmented Feature Sampling (MFS) method. Specifically, EBL increases the intensity of the adjustment of the decision boundary for the weak classes by a designed score-guided loss margin between any two classes. On the other hand, MFS improves the frequency and accuracy of the adjustments of the decision boundary for the weak classes through over-sampling the instance features of those classes. Therefore, EBL and MFS work collaboratively for finding the classification equilibrium in long-tailed detection, and dramatically improve the performance of tail classes while maintaining or even improving the performance of head classes. We conduct experiments on LVIS using Mask R-CNN with various backbones including ResNet-50-FPN and ResNet-101-FPN to show the superiority of the proposed method. It improves the detection performance of tail classes by 15.6 AP, and outperforms the most recent long-tailed object detectors by more than 1 AP. Code is available at https://github.com/fcjian/LOCE.

@InProceedings{Feng_2021_ICCV,
    author    = {Feng, Chengjian and Zhong, Yujie and Huang, Weilin},
    title     = {Exploring Classification Equilibrium in Long-Tailed Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3417-3426}
}


Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren

Abstract: Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., blackbox attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accuracy in a source model without aware of intrinsic features of objects in the images. We argue that such brute-force degradation would introduce model-specific local optimum into adversarial examples, thus limiting the transferability. By contrast, we propose the Feature Importance-aware Attack (FIA), which disrupts important object-aware features that dominate model decisions consistently. More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image. The gradients will be highly correlated to objects of interest, and such correlation presents invariance across different models. Besides, the random transforms will preserve intrinsic features of objects and suppress model-specific information. Finally, the feature importance guides to search for adversarial examples towards disrupting critical features, achieving stronger transferability. Extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed FIA, i.e., improving the success rate by 9.5% against normally trained models and 12.8% against defense models as compared to the state-of-the-art transferable attacks. Code is available at: https://github.com/hcguoO0/FIA

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Zhibo and Guo, Hengchang and Zhang, Zhifei and Liu, Wenxin and Qin, Zhan and Ren, Kui},
    title     = {Feature Importance-Aware Transferable Adversarial Attacks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7639-7648}
}


Meta Gradient Adversarial Attack

Zheng Yuan, Jie Zhang, Yunpei Jia, Chuanqi Tan, Tao Xue, Shiguang Shan

Abstract: In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called Meta Gradient Adversarial Attack (MGAA), which is plug-and-play and can be integrated with any existing gradient-based attack method for improving the cross-model transferability. Specifically, we randomly sample multiple models from a model zoo to compose different tasks and iteratively simulate a white-box attack and a black-box attack in each task. By narrowing the gap between the gradient directions in white-box and black-box attacks, the transferability of adversarial examples on the black-box setting can be improved. Extensive experiments on the CIFAR10 and ImageNet datasets show that our architecture outperforms the state-of-the-art methods for both black-box and white-box attack settings.

@InProceedings{Yuan_2021_ICCV,
    author    = {Yuan, Zheng and Zhang, Jie and Jia, Yunpei and Tan, Chuanqi and Xue, Tao and Shan, Shiguang},
    title     = {Meta Gradient Adversarial Attack},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7748-7757}
}


Attila Lengyel, Sourav Garg, Michael Milford, Jan C. van Gemert

Abstract: We explore the zero-shot setting for day-night domain adaptation. The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set. As gathering relevant test data is expensive and sometimes even impossible, we do not rely on test data and instead exploit a visual inductive prior derived from physics-based reflection models for domain adaptation. We cast a number of color invariant edge detectors as trainable layers in a convolutional neural network and evaluate their robustness to illumination changes. We show that the color invariant layer reduces the day-night distribution shift in feature map activations throughout the network. We demonstrate improved performance for zero-shot day to night domain adaptation on both synthetic as well as natural datasets in various tasks, including classification, segmentation and place recognition.

@InProceedings{Lengyel_2021_ICCV,
    author    = {Lengyel, Attila and Garg, Sourav and Milford, Michael and van Gemert, Jan C.},
    title     = {Zero-Shot Day-Night Domain Adaptation With a Physics Prior},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4399-4409}
}


Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo, Gaofeng Meng, Qi Tian, Weiming Hu, Chunhong Pan

Abstract: Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds. To address these problems, many handcrafted convolution variants have sprung up in recent years. Though with elaborate design, these variants could be far from optimal in sufficiently capturing diverse shapes formed by discrete points. In this paper, we propose PointSeaConv, i.e., a novel differential convolution search paradigm on point clouds. It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling. We also propose a joint optimization framework for simultaneous search of internal convolution and external architecture, and introduce epsilon-greedy algorithm to alleviate the effect of discretization error. As a result, PointSeaNet, a deep network that is sufficient to capture geometric shapes at both convolution level and architecture level, can be searched out for point cloud processing. Extensive experiments strongly evidence that our proposed PointSeaNet surpasses current handcrafted deep models on challenging benchmarks across multiple tasks with remarkable margins.

@InProceedings{Nie_2021_ICCV,
    author    = {Nie, Xing and Liu, Yongcheng and Chen, Shaohong and Chang, Jianlong and Huo, Chunlei and Meng, Gaofeng and Tian, Qi and Hu, Weiming and Pan, Chunhong},
    title     = {Differentiable Convolution Search for Point Cloud Processing},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7437-7446}
}


Yaqing Ding, Daniel Barath, Zuzana Kukelova

Abstract: When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, e.g. smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras can be aligned or assumed to be already aligned, reducing the relative orientation to 1-DOF (degree of freedom). Exploiting this assumption, we propose new minimal solutions to panoramic stitching of images taken by cameras with coinciding optical centers, i.e. undergoing pure rotation. We consider six practical camera configurations, from fully calibrated ones up to a camera with unknown fixed or varying focal length and with or without radial distortion. The solvers are tested both on synthetic scenes, on more than 500k real image pairs from the Sun360 dataset, and from scenes captured by us using two smartphones equipped with IMUs. The new solvers have similar or better accuracy than the state-of-the-art ones and outperform them in terms of processing time.

@InProceedings{Ding_2021_ICCV,
    author    = {Ding, Yaqing and Barath, Daniel and Kukelova, Zuzana},
    title     = {Minimal Solutions for Panoramic Stitching Given Gravity Prior},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5579-5588}
}


Ali Diba, Vivek Sharma, Reza Safdari, Dariush Lotfi, Saquib Sarfraz, Rainer Stiefelhagen, Luc Van Gool

Abstract: In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion. The proposed neural network architecture and objectives are designed to obtain two different Convolutional Neural Networks for solving visual recognition tasks in the domain of videos and images. Our method called Video/Image for Visual Contrastive Learning of Representation(Vi2CLR) uses unlabeled videos to exploit dynamic and static visual cues for self-supervised and instances similarity/dissimilarity learning. Vi2CLR optimization pipeline consists of visual clustering part and representation learning based on groups of similar positive instances within a cluster and negative ones from other clusters and learning visual clusters and their distances. We show how a joint self-supervised visual clustering and instance similarity learning with 2D (image) and 3D (video) CovNet encoders yields such robust and near to supervised learning performance. We extensively evaluate the method on downstream tasks like large scale action recognition and image and object classification on datasets like Kinetics, ImageNet, Pascal VOC'07 and UCF101 and achieve outstanding results compared to state-of-the-art self-supervised methods. To the best of our knowledge, the Vi2CLR is the first of its kind self-supervised neural network to tackle both video and image recognition task simultaneously by only using one source of data.

@InProceedings{Diba_2021_ICCV,
    author    = {Diba, Ali and Sharma, Vivek and Safdari, Reza and Lotfi, Dariush and Sarfraz, Saquib and Stiefelhagen, Rainer and Van Gool, Luc},
    title     = {Vi2CLR: Video and Image for Visual Contrastive Learning of Representation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1502-1512}
}


Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang

Abstract: In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs. The conventional feature imitation paradigm introduces imitation masks to focus on informative foreground areas while excluding the background noises. However, we find that those methods fail to fully utilize the semantic information in all feature pyramid levels, which leads to inefficiency for knowledge distillation between FPN-based detectors. To this end, we propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels to provide the optimal guidance to the student. To push the envelop even further, we introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Finally, we propose a generalized detection KD pipeline, which is capable of distilling both homogeneous and heterogeneous detector pairs. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction; (2) for both homogeneous and heterogenous student-teacher pairs and (3) on multiple detection benchmarks. With a powerful X101-FasterRCNN-Instaboost detector as the teacher, R50-FasterRCNN reaches 44.0% AP, R50-RetinaNet reaches 43.3% AP and R50-FCOS reaches 43.1% AP on COCO dataset.

@InProceedings{Yao_2021_ICCV,
    author    = {Yao, Lewei and Pi, Renjie and Xu, Hang and Zhang, Wei and Li, Zhenguo and Zhang, Tong},
    title     = {G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-Guided Feature Imitation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3591-3600}
}


Jeesoo Kim, Junsuk Choe, Sangdoo Yun, Nojun Kwak

Abstract: Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information. By simply training a classification model using only image-level annotations, the feature map of a model can be utilized as a score map for localization. In spite of many WSOL methods proposing novel strategies, there has not been any de facto standards about how to normalize the class activation map (CAM). Consequently, many WSOL methods have failed to fully exploit their own capacity because of the misuse of a normalization method. In this paper, we review many existing normalization methods and point out that they should be used according to the property of the given dataset. Additionally, we propose a new normalization method which substantially enhances the performance of any CAM-based WSOL methods. Using the proposed normalization method, we provide a comprehensive evaluation over three datasets (CUB, ImageNet and OpenImages) on three different architectures and observe significant performance gains over the conventional normalization methods in all the evaluated cases.

@InProceedings{Kim_2021_ICCV,
    author    = {Kim, Jeesoo and Choe, Junsuk and Yun, Sangdoo and Kwak, Nojun},
    title     = {Normalization Matters in Weakly Supervised Object Localization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3427-3436}
}


Kai Hu, Jie Shao, Yuan Liu, Bhiksha Raj, Marios Savvides, Zhiqiang Shen

Abstract: This paper studies the problem of learning self-supervised representations on videos. In contrast to image modality that only requires appearance information on objects or scenes, video needs to further explore the relations between multiple frames/clips along the temporal dimension. However, the recent proposed contrastive-based self-supervised frameworks do not grasp such relations explicitly since they simply utilize two augmented clips from the same video and compare their distance without referring to their temporal relation. To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames. In particular, given two video clips, our model first predicts whether they come from the same input video, and then predict the temporal ordering of the clips if they come from the same video. We also propose a novel decoupling attention method to learn symmetric similarity (contrast) and anti-symmetric patterns (order). Such design involves neither extra parameters nor computation, but can speed up the learning process and improve accuracy compared to the vanilla multi-head attention. We extensively validate the representation ability of our learned video features for the downstream action recognition task on Kinetics-400 and Something-something V2. Our method outperforms previous state-of-the-arts by a significant margin.

@InProceedings{Hu_2021_ICCV,
    author    = {Hu, Kai and Shao, Jie and Liu, Yuan and Raj, Bhiksha and Savvides, Marios and Shen, Zhiqiang},
    title     = {Contrast and Order Representations for Video Self-Supervised Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7939-7949}
}


Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht

Abstract: The minimum graph cut and minimum s-t-cut problems are important primitives in the modeling of combinatorial problems in computer science, including in computer vision and machine learning. Some of the most efficient algorithms for finding global minimum cuts are randomized algorithms based on Karger's groundbreaking contraction algorithm. Here, we study whether Karger's algorithm can be successfully generalized to other cut problems. We first prove that a wide class of natural generalizations of Karger's algorithm cannot efficiently solve the s-t-mincut or the normalized cut problem to optimality. However, we then present a simple new algorithm for seeded segmentation / graph-based semi-supervised learning that is closely based on Karger's original algorithm, showing that for these problems, extensions of Karger's algorithm can be useful. The new algorithm has linear asymptotic runtime and yields a potential that can be interpreted as the posterior probability of a sample belonging to a given seed / class. We clarify its relation to the random walker algorithm / harmonic energy minimization in terms of distributions over spanning forests. On classical problems from seeded image segmentation and graph-based semi-supervised learning on image data, the method performs at least as well as the random walker / harmonic energy minimization / Gaussian processes.

@InProceedings{Jenner_2021_ICCV,
    author    = {Jenner, Erik and Sanmart{\'\i}n, Enrique Fita and Hamprecht, Fred A.},
    title     = {Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4602-4611}
}


Abstract: We present an out-of-core variational approach for surface reconstruction from a set of aligned depth maps. Input depth maps are supposed to be reconstructed from regular photos or/and can be a representation of terrestrial LIDAR point clouds. Our approach is based on surface reconstruction via total generalized variation minimization (TGV) because of its strong visibility-based noise-filtering properties and GPU-friendliness. Our main contribution is an out-of-core OpenCL-accelerated adaptation of this numerical algorithm which can handle arbitrarily large real-world scenes with scale diversity.

@InProceedings{Poliarnyi_2021_ICCV,
    author    = {Poliarnyi, Nikolai},
    title     = {Out-of-Core Surface Reconstruction via Global TGV Minimization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5641-5650}
}


Abstract: Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes intact. Current methods achieve either: (i) disentanglement, which exhibits low visual fidelity and can only be satisfied where the attributes are perfectly uncorrelated. (ii) visually-plausible translations, which are clearly not disentangled. In this work, we propose OverLORD, a single framework for disentangling labeled and unlabeled attributes as well as synthesizing high-fidelity images, which is composed of two stages; (i) Disentanglement: Learning disentangled representations with latent optimization. Differently from previous approaches, we do not rely on adversarial training or any architectural biases. (ii) Synthesis: Training feed-forward encoders for inferring the learned attributes and tuning the generator in an adversarial manner to increase the perceptual quality. When the labeled and unlabeled attributes are correlated, we model an additional representation that accounts for the correlated attributes and improves disentanglement. We highlight that our flexible framework covers multiple settings as disentangling labeled attributes, pose and appearance, localized concepts, and shape and texture. We present significantly better disentanglement with higher translation quality and greater output diversity than state-of-the-art methods.

@InProceedings{Gabbay_2021_ICCV,
    author    = {Gabbay, Aviv and Hoshen, Yedid},
    title     = {Scaling-Up Disentanglement for Image Translation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6783-6792}
}


Alexander Richard, Michael Zollhöfer, Yandong Wen, Fernando de la Torre, Yaser Sheikh

Abstract: This paper presents a generic method for generating full facial 3D animation from speech. Existing approaches to audio-driven facial animation exhibit uncanny or static upper face animation, fail to produce accurate and plausible co-articulation or rely on person-specific models that limit their scalability. To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face. At the core of our approach is a categorical latent space for facial animation that disentangles audio-correlated and audio-uncorrelated information based on a novel cross-modality loss. Our approach ensures highly accurate lip motion, while also synthesizing plausible animation of the parts of the face that are uncorrelated to the audio signal, such as eye blinks and eye brow motion. We demonstrate that our approach outperforms several baselines and obtains state-of-the-art quality both qualitatively and quantitatively. A perceptual user study demonstrates that our approach is deemed more realistic than the current state-of-the-art in over 75% of cases. We recommend watching the supplemental video before reading the paper: https://github.com/facebookresearch/meshtalk

@InProceedings{Richard_2021_ICCV,
    author    = {Richard, Alexander and Zollh\"ofer, Michael and Wen, Yandong and de la Torre, Fernando and Sheikh, Yaser},
    title     = {MeshTalk: 3D Face Animation From Speech Using Cross-Modality Disentanglement},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1173-1182}
}