mtybilly/Instruct2DS huggingface dataset:
Continuous Scene Representations for Embodied AI
Authors: Kiana Ehsani, Shuran Song, Roozbeh Mottaghi Samir Yitzhak Gadre
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We propose Continuous Scene Representations (CSR), a scene representation constructed by an embodied agent navigating within a space, where objects and their relationships are modeled by continuous valued embeddings. Our method captures feature relationships between objects, composes them into a graph structure on-the-fly, and situates an embodied agent within the representation. Our key insight is to embed pair-wise relationships between objects in a latent space. This allows for a richer representation compared to discrete relations (e.g., [support], [next-to]) commonly used for building scene representations. CSR can track objects as the agent moves in a scene, update the representation accordingly, and detect changes in room configurations. Using CSR, we outperform state-of-the-art approaches for the challenging downstream task of visual room rearrangement, without any task specific training. Moreover, we show the learned embeddings capture salient spatial details of the scene and show applicability to real world data. A summery video and code is available at https://prior.allenai.org/projects/csr.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Authors: Xu Yan, Haiming Zhang, Baoyuan Wang, Shenghui Cheng, Shuguang Cui, Zhen Li Chaoda Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: 3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M^2-Track. At the 1^st-stage, M^2-Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2^nd-stage. Extensive experiments confirm that M^2-Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS ( 8%,  17%, and  22%) precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). Further analysis verifies each component's effectiveness and shows the motion-centric paradigm's promising potential when combined with appearance matching. Code will be made available at https://github.com/Ghostish/Open3DSOT.
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
Authors: Junfeng Guo, Ang Li, Wei-Te Ting, Cong Liu, H.T. Kung Xin Dong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training set, and optimization objectives. Deviating from existing work, we have a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the training set distribution which can be leveraged for reliable OOD detection. Our empirical study on validating this hypothesis, which measures the model activation's mean for OOD and in-distribution (ID) mini-batches, surprisingly finds that activation means of OOD mini-batches consistently deviate more from those of the training data. In addition, training data's activation means can be computed offline efficiently or retrieved from batch normalization layers as a "free lunch". Based upon this observation, we propose a novel metric called Neural Mean Discrepancy (NMD), which compares neural means of the input examples and training data. Leveraging the simplicity of NMD, we propose an efficient OOD detector that computes neural means by a standard forward pass followed by a lightweight classifier. Extensive experiments show that NMD outperforms state-of-the-art OOD approaches across multiple datasets and model architectures in terms of both detection accuracy and computational cost.
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction
Authors: Jin-Hwi Park, Hae-Gon Jeon Inhwan Bae
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions of pedestrians in the inference step. However, stochastic approaches provide varying results for the same data and parameter settings, due to the random sampling of the latent vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively. Through this analysis, we observe that the inferences of all stochastic models are biased toward the random sampling, and fail to generate a set of realistic paths from finite samples. The problem cannot be resolved unless an infinite number of samples is available, which is infeasible in practice. We introduce that the Quasi-Monte Carlo (QMC) method, ensuring uniform coverage on the sampling space, as an alternative to the conventional random sampling. With the same finite number of samples, the QMC improves all the multimodal prediction results. We take an additional step ahead by incorporating a learnable sampling network into the existing networks for trajectory prediction. For this purpose, we propose the Non-Probability Sampling Network (NPSN), a very small network ( 5K parameters) that generates purposive sample sequences using the past paths of pedestrians and their social interactions. Extensive experiments confirm that NPSN can significantly improve both the prediction accuracy (up to 60%) and reliability of the public pedestrian trajectory prediction benchmark. Code is publicly available at https://github.com/inhwanbae/NPSN.
Marginal Contrastive Correspondence for Guided Image Generation
Authors: Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang Fangneng Zhan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Exemplar-based image translation establishes dense correspondences between a conditional input and an exemplar (from two different domains) for leveraging detailed exemplar styles to achieve realistic image translation. Existing work builds the cross-domain correspondences implicitly by minimizing feature-wise distances across the two domains. Without explicit exploitation of domain-invariant features, this approach may not reduce the domain gap effectively which often leads to sub-optimal correspondences and image translation. We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation. Specifically, we design an innovative marginal contrastive loss that guides to establish dense correspondences explicitly. Nevertheless, building correspondence with domain-invariant semantics alone may impair the texture patterns and lead to degraded texture generation. We thus design a Self-Correlation Map (SCM) that incorporates scene structures as auxiliary information which improves the built correspondences substantially. Quantitative and qualitative experiments on multifarious image translation tasks show that the proposed method outperforms the state-of-the-art consistently.
Complex Backdoor Detection by Symmetric Feature Differencing
Authors: Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, Xiangyu Zhang Yingqi Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Many existing backdoor scanners work by finding a small and fixed trigger. However, advanced attacks have large and pervasive triggers, rendering existing scanners less effective. We develop a new detection method. It first uses a trigger inversion technique to generate triggers, namely, universal input patterns flipping victim class samples to a target class. It then checks if any such trigger is composed of features that are not natural distinctive features between the victim and target classes. It is based on a novel symmetric feature differencing method that identifies features separating two sets of samples (e.g., from two respective classes). We evaluate the technique on a number of advanced attacks including composite attack, reflection attack, hidden attack, filter attack, and also on the traditional patch attack. The evaluation is on thousands of models, including both clean and trojaned models, with various architectures. We compare with three state-of-the-art scanners. Our technique can achieve 80-88% accuracy while the baselines can only achieve 50-70% on complex attacks. Our results on the TrojAI competition rounds 2-4, which have patch backdoors and filter backdoors, show that existing scanners may produce hundreds of false positives (i.e., clean models recognized as trojaned), while our technique removes 78-100% of them with a small increase of false negatives by 0-30%, leading to 17-41% overall accuracy improvement. This allows us to achieve top performance on the leaderboard.
Time Lens++: Event-Based Frame Interpolation With Parametric Non-Linear Flow and Multi-Scale Fusion
Authors: Alfredo Bochicchio, Daniel Gehrig, Stamatios Georgoulis, Yuanyou Li, Davide Scaramuzza Stepan Tulyakov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and far-away scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion---which can be efficiently sampled for image warping---from events and images. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and by up to 15% in LPIPS score. Code and dataset will be released upon acceptance.
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
Authors: Adnan Siraj Rakin, Xing Chen, Zhezhi He, Deliang Fan, Chaitali Chakrabarti Jingtao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low computation overhead. Code is released at: https://github.com/zlijingtao/ResSFL
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
Authors: Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, Xuhui Huang Yufei Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the synaptic activity of biological neurons, which transmits binary spike signals between network units when the membrane potential exceeds the firing threshold. This bio-mimetic mechanism of SNN appears energy-efficiency with its power sparsity and asynchronous operations on spike events. Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence. Such undesired shifts would prevent the SNN from performing well and going deep. To tackle these problems, we attempt to rectify the membrane potential distribution (MPD) by designing a novel distribution loss, MPD-Loss, which can explicitly penalize the undesired shifts without introducing any additional operations in the inference phase. Moreover, the proposed method can also mitigate the quantization error in SNNs, which is usually ignored in other works. Experimental results demonstrate that the proposed method can directly train a deeper, larger and better performing SNN within fewer timesteps.
Human-Aware Object Placement for Visual Environment Reconstruction
Authors: Chun-Hao P. Huang, Dimitrios Tzionas, Muhammed Kocabas, Mohamed Hassan, Siyu Tang, Justus Thies, Michael J. Black Hongwei Yi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Humans are in constant contact with the world as they move through it and interact with it. This contact is a vital source of information for understanding 3D humans, 3D scenes, and the interactions between them. In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video. Our key idea is that, as a person moves through a scene and interacts with it, we accumulate HSIs across multiple input images, and use these in optimizing the 3D scene to reconstruct a consistent, physically plausible, 3D scene layout. Our optimization-based approach exploits three types of HSI constraints: (1) humans who move in a scene are occluded by, or occlude, objects, thus constraining the depth ordering of the objects, (2) humans move through free space and do not interpenetrate objects, (3) when humans and objects are in contact, the contact surfaces occupy the same place in space. Using these constraints in an optimization formulation across all observations, we significantly improve 3D scene layout reconstruction. Furthermore, we show that our scene reconstruction can be used to refine the initial 3D human pose and shape (HPS) estimation. We evaluate the 3D scene layout reconstruction and HPS estimates qualitatively and quantitatively using the PROX and PiGraphs datasets. The code and data are available for research purposes at https://mover.is.tue.mpg.de.
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Authors: Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu Satya Krishna Gorti
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos. Therefore, for a given text, a retrieval model should focus on the text's most semantically similar video sub-regions to make a more relevant comparison. Yet, most existing works aggregate entire videos without directly considering text. Common text-agnostic aggregations schemes include mean-pooling or self-attention over the frames, but these are likely to encode misleading visual information not described in the given text. To address this, we propose a cross-modal attention model called X-Pool that reasons between a text and the frames of a video. Our core mechanism is a scaled dot product attention for a text to attend to its most semantically similar frames. We then generate an aggregated video representation conditioned on the text's attention weights over the frames. We evaluate our method on three benchmark datasets of MSR-VTT, MSVD and LSMDC, achieving new state-of-the-art results by up to 12% in relative improvement in Recall@1. Our findings thereby highlight the importance of joint text-video reasoning to extract important visual cues according to text. Full code and demo can be found at: https://layer6ai-labs.github.io/xpool/
Learning of Global Objective for Network Flow in Multi-Object Tracking
Authors: Yu Kong, Hamid Rezatofighi Shuai Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This paper concerns the problem of multi-object tracking based on the min-cost flow (MCF) formulation, which is conventionally studied as an instance of linear program. Given its computationally tractable inference, the success of MCF tracking largely relies on the learned cost function of underlying linear program. Most previous studies focus on learning the cost function by only taking into account two frames during training, therefore the learned cost function is sub-optimal for MCF where a multi-frame data association must be considered during inference. In order to address this problem, in this paper we propose a novel differentiable framework that ties training and inference together during learning by solving a bi-level optimization problem, where the lower-level solves a linear program and the upper-level contains a loss function that incorporates global tracking result. By back-propagating the loss through differentiable layers via gradient descent, the globally parameterized cost function is explicitly learned and regularized. With this approach, we are able to learn a better objective for global MCF tracking. As a result, we achieve competitive performances compared to the current state-of-the-art methods on the popular multi-object tracking benchmarks such as MOT16, MOT17 and MOT20
Towards Weakly-Supervised Text Spotting Using a Multi-Task Transformer
Authors: Inbal Lavi, Sharon Fogel, Yarin Bar, R. Manmatha, Pietro Perona Yair Kittenplon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.
Gated2Gated: Self-Supervised Depth Estimation From Gated Images
Authors: Stefanie Walz, Mario Bijelic, Fahim Mannan, Frank Julca-Aguilar, Michael Langer, Werner Ritter, Felix Heide Amanpreet Walia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth in the relative intensity of a handful of gated slices, captured at megapixel resolution. Although existing methods have shown that it is possible to decode high-resolution depth from such measurements, these methods require synchronized and calibrated LiDAR to supervise the gated depth decoder - prohibiting fast adoption across geographies, training on large unpaired datasets, and exploring alternative applications outside of automotive use cases. In this work, we fill this gap and propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal. The proposed model is trained end-to-end from gated video sequences, does not require LiDAR or RGB data, and learns to estimate absolute depth values. We take gated slices as input and disentangle the estimation of the scene albedo, depth, and ambient light, which are then used to learn to reconstruct the input slices through a cyclic loss. We rely on temporal consistency between a given frame and neighboring gated slices to estimate depth in regions with shadows and reflections. We experimentally validate that the proposed approach outperforms existing supervised and self-supervised depth estimation methods based on monocular RGB and stereo images, as well as supervised methods based on gated images.
RAMA: A Rapid Multicut Algorithm on GPU
Authors: Paul Swoboda Ahmed Abbas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We propose a highly parallel primal-dual algorithm for the multicut (a.k.a. correlation clustering) problem, a classical graph clustering problem widely used in machine learning and computer vision. Our algorithm consists of three steps executed recursively: (1) Finding conflicted cycles that correspond to violated inequalities of the underlying multicut relaxation, (2) Performing message passing between the edges and cycles to optimize the Lagrange relaxation coming from the found violated cycles producing reduced costs and (3) Contracting edges with high reduced costs through matrix-matrix multiplications. Our algorithm produces primal solutions and lower bounds that estimate the distance to optimum. We implement our algorithm on GPUs and show resulting one to two orders-of-magnitudes improvements in execution speed without sacrificing solution quality compared to traditional sequential algorithms that run on CPUs. We can solve very large scale benchmark problems with up to O(10^8) variables in a few seconds with small primal-dual gaps. Our code is available at https://github.com/pawelswoboda/RAMA.
Adversarial Parametric Pose Prior
Authors: Anastasia Remizova, Victor Constantin, Sina Honari, Mathieu Salzmann, Pascal Fua Andrey Davydov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is under-constrained and may yield invalid results. We propose learning a prior that restricts the SMPL parameters to values that produce realistic poses via adversarial training. We show that our learned prior covers the diversity of the real-data distribution, facilitates optimization for 3D reconstruction from 2D keypoints, and yields better pose estimates when used for regression from images. For all these tasks, it outperforms the state-of-the-art VAE-based approach to constraining the SMPL parameters. The code will be made available at https://github.com/cvlab-epfl/adv_param_pose_prior.
DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning
Authors: Luping Zhou, Yue Duan, Lei Wang, Lei Qi, Yinghuan Shi Zhen Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Consistency-based Semi-supervised learning (SSL) has achieved promising performance recently. However, the success largely depends on the assumption that the labeled and unlabeled data share an identical class distribution, which is hard to meet in real practice. The distribution mismatch between the labeled and unlabeled sets can cause severe bias in the pseudo-labels of SSL, resulting in significant performance degradation. To bridge this gap, we put forward a new SSL learning framework, named Distribution Consistency SSL (DC-SSL), which rectifies the pseudo-labels from a distribution perspective. The basic idea is to directly estimate a reference class distribution (RCD), which is regarded as a surrogate of the ground truth class distribution about the unlabeled data, and then improve the pseudo-labels by encouraging the predicted class distribution (PCD) of the unlabeled data to approach RCD gradually. To this end, this paper revisits the Exponentially Moving Average (EMA) model and utilizes it to estimate RCD in an iteratively improved manner, which is achieved with a momentum-update scheme throughout the training procedure. On top of this, two strategies are proposed for RCD to rectify the pseudo-label prediction, respectively. They correspond to an efficient training-free scheme and a training-based alternative that generates more accurate and reliable predictions. DC-SSL is evaluated on multiple SSL benchmarks and demonstrates remarkable performance improvement over competitive methods under matched- and mismatched-distribution scenarios.
Mask Transfiner for High-Quality Instance Segmentation
Authors: Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu Lei Ke
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Transfiner for high-quality and efficient instance segmentation. Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree. Our transformer-based approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. While these sparse pixels only constitute a small proportion of the total number, they are critical to the final mask quality. This allows Mask Transfiner to predict highly accurate instance masks, at a low computational cost. Extensive experiments demonstrate that Mask Transfiner outperforms current instance segmentation methods on three popular benchmarks, significantly improving both two-stage and query-based frameworks by a large margin of +3.0 mask AP on COCO and BDD100K, and +6.6 boundary AP on Cityscapes. Our code and trained models are available at https://github.com/SysCV/transfiner.
End-to-End Reconstruction-Classification Learning for Face Forgery Detection
Authors: Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, Xiaokang Yang Junyi Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Existing face forgery detectors mainly focus on specific forgery patterns like noise characteristics, local textures, or frequency statistics for forgery detection. This causes specialization of learned representations to known forgery patterns presented in the training set, and makes it difficult to detect forgeries with unknown patterns. In this paper, from a new perspective, we propose a forgery detection framework emphasizing the common compact representations of genuine faces based on reconstruction-classification learning. Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries. To achieve better representations, instead of only using the encoder in reconstruction learning, we build bipartite graphs over the encoder and decoder features in a multi-scale fashion. We further exploit the reconstruction difference as guidance of forgery traces on the graph output as the final representation, which is fed into the classifier for forgery detection. The reconstruction and classification learning is optimized end-to-end. Extensive experiments on large-scale benchmark datasets demonstrate the superiority of the proposed method over state of the arts.
It Is Okay To Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection
Authors: Faizan Farooq Khan, Kilichbek Haydarov, Mohamed Elhoseiny Youssef Mohamed
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions. We observed a significant emotional bias towards instance-rich emotions, making trained neural speakers less accurate in describing under-represented emotions. We show that collecting new data, in the same way, is not effective in mitigating this emotional bias. To remedy this problem, we propose a contrastive data collection approach to balance ArtEmis with a new complementary dataset such that a pair of similar images have contrasting emotions (one positive and one negative). We collected 260,533 instances using the proposed method, we combine them with ArtEmis, creating a second iteration of the dataset. The new combined dataset, dubbed ArtEmis v2.0, has a balanced distribution of emotions with explanations revealing more fine details in the associated painting. Our experiments show that neural speakers trained on the new dataset improve CIDEr and METEOR evaluation metrics by 20% and 7%, respectively, compared to the biased dataset. Finally, we also show that the performance per emotion of neural speakers is improved across all the emotion categories, significantly on under-represented emotions. The collected dataset and code are available at https://artemisdataset-v2.org.
Transferability Metrics for Selecting Source Model Ensembles
Authors: Jasper Uijlings, Thomas Mensink, Vittorio Ferrari Andrea Agostinelli
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the target training set, yields the best performance on the target test set. Since fine-tuning all possible ensembles is computationally prohibitive, we aim at predicting performance on the target dataset using a computationally efficient transferability metric. We propose several new transferability metrics designed for this task and evaluate them in a challenging and realistic transfer learning setup for semantic segmentation: we create a large and diverse pool of source models by considering 17 source datasets covering a wide variety of image domain, two different architectures, and two pre-training schemes. Given this pool, we then automatically select a subset to form an ensemble performing well on a given target dataset. We compare the ensemble selected by our method to two baselines which select a single source model, either (1) from the same pool as our method; or (2) from a pool containing large source models, each with similar capacity as an ensemble. Averaged over 17 target datasets, we outperform these baselines by 6.0% and 2.5% relative mean IoU, respectively.
Neural Global Shutter: Learn To Restore Video From a Rolling Shutter Camera With Global Reset Feature
Authors: Xiang Ji, Jia-Bin Huang, Shin'ichi Satoh, Xiao Zhou, Yinqiang Zheng Zhixiang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera and object undergo motion during capture. Extensive researches have been conducted on correcting RS distortions. However, most of the existing work relies heavily on the prior assumptions of scenes or motions. Besides, the motion estimation steps are either oversimplified or computationally inefficient due to the heavy flow warping, limiting their applicability. In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos. This feature enables us to turn the rectification problem into a deblur-like one, getting rid of inaccurate and costly explicit motion estimation. First, we build an optic system that captures paired RSGR/GS videos. Second, we develop a novel algorithm incorporating spatial and temporal designs to correct the spatial-varying RSGR distortion. Third, we demonstrate that existing image-to-image translation algorithms can recover clean GS videos from distorted RSGR inputs, yet our algorithm achieves the best performance with the specific designs. Our rendered results are not only visually appealing but also beneficial to downstream tasks. Compared to the state-of-the-art RS solution, our RSGR solution is superior in both effectiveness and efficiency. Considering it is easy to realize without changing the hardware, we believe our RSGR solution can potentially replace the RS solution in taking distortion-free videos with low noise and low budget.
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis
Authors: Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang Fatemeh Haghighi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA.
Open Challenges in Deep Stereo: The Booster Dataset
Authors: Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano Pierluigi Zama Ramirez
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.
Location-Free Human Pose Estimation
Authors: Yingguo Gao, Ke Yan, Xue Lin, Qi Zou Xixia Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate this issue, we revisit HPE and propose a location-free framework without supervision of keypoint locations. We reformulate the regression-based HPE from the perspective of classification. Inspired by the CAM-based weakly-supervised object localization, we observe that the coarse keypoint locations can be acquired through the part-aware CAMs but unsatisfactory due to the gap between the fine-grained HPE and the object-level localization. To this end, we propose a customized transformer framework to mine the fine-grained representation of human context, equipped with the structural relation to capture subtle differences among keypoints. Concretely, we design a Multi-scale Spatial-guided Context Encoder to fully capture the global human context while focusing on the part-aware regions and a Relation-encoded Pose Prototype Generation module to encode the structural relations. All these works together for strengthening the weak supervision from image-level category labels on locations. Our model achieves competitive performance on three datasets when only supervised at a category-level and importantly, it can achieve comparable results with fully-supervised methods with only 25% location labels on MS-COCO and MPII.
Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography
Authors: Kicheon Park, Yingtian Pan, Haibin Ling Jiaxiang Ren
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art methods usually treat BMA removal as a learning-based image inpainting problem, but require numerous training samples with nontrivial annotation. In addition, these methods discard the rich structural and appearance information carried in the BMA stripe region. To address these issues, in this paper we propose a self-supervised content-aware BMA removal model. First, the gradient-based structural information and appearance feature are extracted from the BMA area and injected into the model to capture more connectivity. Second, with easily collected defective masks, the model is trained in a self-supervised manner, in which only the clear areas are used for training while the BMA areas for inference. With the structural information and appearance feature from noisy image as references, our model can remove larger BMA and produce better visualizing result. In addition, only 2D images with defective masks are involved, hence improving the efficiency of our method. Experiments on OCTA of mouse cortex demonstrate that our model can remove most BMA with extremely large sizes and inconsistent intensities while previous methods fail.
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects
Authors: Umar Iqbal, Jonathan Tremblay, Tatsuya Harada, Orazio Gallo Atsuhiro Noguchi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies. Manipulating the pose of an object, however, requires the understanding of its underlying structure, that is, its joints and how they interact with each other. Unfortunately, assuming the structure to be known, as existing methods do, precludes the ability to work on new object categories. We propose to learn both the appearance and the structure of previously unseen articulated objects by observing them move from multiple views, with no joints annotation supervision, or information about the structure. We observe that 3D points that are static relative to one another should belong to the same part, and that adjacent parts that move relative to each other must be connected by a joint. To leverage this insight, we model the object parts in 3D as ellipsoids, which allows us to identify joints. We combine this explicit representation with an implicit one that compensates for the approximation introduced. We show that our method works for different structures, from quadrupeds, to single-arm robots, to humans.
PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking
Authors: Di Chen, Shanshan Zhang, Bernt Schiele, Jürgen Gall Andreas Döring
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e.g. person detection or appearance-based association of detected persons. Consequently, approaches on these respective tasks are eligible to complement each other. Therefore, we introduce PoseTrack21, a large-scale dataset for person search, multi-object tracking and multi-person pose tracking in real-world scenarios with a high diversity of poses. The dataset provides rich annotations like human pose annotations including annotations of joint occlusions, bounding box annotations even for small persons, and person-ids within and across video sequences. The dataset allows to evaluate multi-object tracking and multi-person pose tracking jointly with person re-identification or exploit structural knowledge of human poses to improve person search and tracking, particularly in the context of severe occlusions. With PoseTrack21, we want to encourage researchers to work on joint approaches that perform reasonably well on all three tasks.
Event-Based Video Reconstruction via Potential-Assisted Spiking Neural Network
Authors: Xiao Wang, Yi Chang, Jianing Li, Tiejun Huang, Yonghong Tian Lin Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called 'events' with high temporal resolution and high dynamic range. So far, the event-based image reconstruction methods are based on artificial neural networks (ANN) or hand-crafted spatiotemporal smoothing techniques. In this paper, we first implement the image reconstruction work via deep spiking neural network (SNN) architecture. As the bio-inspired neural networks, SNNs operating with asynchronous binary spikes distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron. We find that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks. Furthermore, to better utilize the temporal information, we propose a hybrid potential-assisted framework (PA-EVSNN) using the membrane potential of spiking neuron. The proposed neuron is referred as Adaptive Membrane Potential (AMP) neuron, which adaptively updates the membrane potential according to the input spikes. The experimental results demonstrate that our models achieve comparable performance to ANN-based models on IJRR, MVSEC, and HQF datasets. The energy consumptions of EVSNN and PA-EVSNN are 19.36 times and 7.75 times more computationally efficient than their ANN architectures, respectively. The code and pretrained model are available at https://sites.google.com/view/evsnn.
Efficient Maximal Coding Rate Reduction by Variational Forms
Authors: Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele Christina Baek
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The principle of Maximal Coding Rate Reduction (MCR2) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high-dimensional data to allow for more robust training than standard approaches, such as cross-entropy minimization. However, despite the advantages that have been shown for MCR2 training, MCR2 suffers from a significant computational cost due to the need to evaluate and differentiate a significant number of log-determinant terms that grows linearly with the number of classes. By taking advantage of variational forms of spectral functions of a matrix, we reformulate the MCR2 objective to a form that can scale significantly without compromising training accuracy. Experiments in image classification demonstrate that our proposed formulation results in a significant speed up over optimizing the original MCR2 objective directly and often results in higher quality learned representations. Further, our approach may be of independent interest in other models that require computation of log-determinant forms, such as in system identification or normalizing flow models.