mtybilly/Instruct2DS huggingface dataset:
"Fashionformer: A Simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition"
Authors: Shilin Xu, Xiangtai Li, Jingbo Wang, Guangliang Cheng, Yunhai Tong, Dacheng Tao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Human fashion understanding is one important computer vision task since it has the comprehensive information for real-world applications. In this work, we focus on joint human fashion segmentation and attribute recognition. Contrary to the previous works that separately model each task as a multi-head prediction problem, our insight is to bridge these two tasks with one unified model via vision transformer modeling to benefit each task. In particular, we introduce the object query for segmentation and the attribute query for attribute prediction. Both queries and their corresponding features can be linked via mask prediction. Then we adopta two-stream query learning framework to learn the decoupled query representations. For attribute stream, we design a novel Multi-Layer Rendering module to explore more fine-grained features. The decoder design shares the same spirits with DETR, thus we name the proposed method \textit{Fahsionformer}. Extensive experiments on three human fashion datasets illustrate the effectiveness of our approach. In particular, our method with the same backbone achieverelative 10% improvements than previous works in case of \textit{a joint metric (AP^{{mask}}_{IoU+F_1}) for both segmentation and attribute recognition}. To the best of our knowledge, we are the first unified end-to-end vision transformer framework for human fashion analysis. We hope this simple yet effective method can serve as a new flexible baseline for fashion analysis. Code will be available at https://github.com/xushilin1/FashionFormer."
Semantic-Guided Multi-Mask Image Harmonization
Authors: Xuqian Ren, Yifan Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Previous harmonization methods focus on adjusting one inharmonious region in an image based on an input mask. They may face problems when dealing with different perturbations on different semantic regions without available input masks. To deal with the problem that one image has been pasted with several foregrounds coming from different images and needs to harmonize them towards different domain directions without any mask as input, we propose a new semantic-guided multi-mask image harmonization task. Different from the previous single-mask image harmonization task, each inharmonious image is perturbed with different methods according to the semantic segmentation masks. Two challenging benchmarks, HScene and HLIP, are constructed based on 150 and 19 semantic classes, respectively. Furthermore, previous baselines focus on regressing the exact value for each pixel of the harmonized images. The generated results are in the ‘black box’ and cannot be edited. In this work, we propose a novel way to edit the inharmonious images by predicting a series of operator masks. The masks indicate the level and the position to apply a certain image editing operation, which could be the brightness, the saturation, and the color in a specific dimension. The operator masks provide more flexibility for users to edit the image further. Extensive experiments verify that the operator mask-based network can further improve those state-of-the-art methods which directly regress RGB images when the perturbations are structural. Experiments have been conducted on our constructed benchmarks to verify that our proposed operator mask-based framework can locate and modify the inharmonious regions in more complex scenes. Our code and models are available at https://github.com/XuqianRen/Semantic-guided-Multi-mask-Image-Harmonization.git."
Learning an Isometric Surface Parameterization for Texture Unwrapping
Authors: Sagnik Das, Ke Ma, Zhixin Shu, Dimitris Samaras
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In this paper, we present a novel approach to learn texture mapping for an isometrically deformed 3D surface and apply it for texture unwrapping of documents or other objects. Recent work on differentiable rendering techniques for implicit surfaces has shown high-quality 3D scene reconstruction and view synthesis results. However, these methods typically learn the appearance color as a function of the surface points and lack explicit surface parameterization. Thus they do not allow texture map extraction or texture editing. We propose an efficient method to learn surface parameterization by learning a continuous bijective mapping between 3D surface positions and 2D texture-space coordinates. Our surface parameterization network can be conveniently plugged into a differentiable rendering pipeline and trained using multi-view images and rendering loss. Using the learned parameterized implicit 3D surface we demonstrate state-of-the-art document-unwarping via texture extraction in both synthetic and real scenarios. We also show that our approach can reconstruct high-frequency textures for arbitrary objects. We further demonstrate the usefulness of our system by applying it to document and object texture editing. Code and related assets are available at: https://github.com/cvlab-stonybrook/Iso-UVField"
Towards Regression-Free Neural Networks for Diverse Compute Platforms
Authors: Rahul Duggal, Hao Zhou, Shuo Yang, Jun Fang, Yuanjun Xiong, Wei Xia
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important. Our work tackles the emergent problem of reducing predictive in-consistencies arising as negative flips: test samples that are correctly predicted by a less accurate on-device model, but incorrectly by a more accurate on-cloud one. We introduce REGression constrained Neural Architecture Search (REG-NAS) to design a family of highly accurate models that engender fewer negative flips. REG-NAS consists of two components: (1) A novel architecture constraint that enables a larger on-cloud model to contain all the weights of the smaller on-device one thus maximizing weight sharing. This idea stems from our observation that larger weight sharing among networks leads to similar sample-wise predictions and results in fewer negative flips; (2) A novel search reward that incorporates both Top-1 accuracy and negative flips in the architecture optimization metric. We demonstrate that REG-NAS can successfully find architecture with few negative flips, in three popular architecture search spaces. Compared to the existing state-of-the-art approach [29], REG-NAS leads to 33-48% relative reduction of negative flips."
Relationship Spatialization for Depth Estimation
Authors: Xiaoyu Xu, Jiayan Qiu, Xinchao Wang, Zhou Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Considering the role played by the relationships between objects in monocular depth estimation (MDE), it can be easily told that relationships, such as ‘in front of’ and ‘behind’, provide explicit spatial priors for depth estimation. However, it is hard to answer the questions that which kinds of relationships embed with the useful spatial cues for MDE? And how much these relationships contribute to the MDE? We term the task of answering these two questions as Relationship Spatialization. To this end, we strive to spatializing the relationships by devising a novel learning-based framework. Specifically, given the monocular image, the image representations and the corresponding scene graph are firstly extracted, and the in-graph relationship representations are learnt to be obtained. Then, the relationship representations from the graph space are spatially aligned with the image representations from the visual space, followed by a redundancy elimination. Finally, we feed the concatenation of the image representations and the modified relationship representations into a depth predictor, which estimates the monocular depth with relationship spatialization. Experiments on KITTI, NYU v2 and ICL-NUIM datasets shows the effectiveness of relationship spatialization on MDE. Moreover, adopting our framework to current state-of-the-art MDE models leads to marginal improvement on most evaluation metrics."
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
Authors: Chenfeng Xu, Shijia Yang, Tomer Galanti, Bichen Wu, Xiangyu Yue, Bohan Zhai, Wei Zhan, Peter Vajda, Kurt Keutzer, Masayoshi Tomizuka
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed for 2D image and 3D point-cloud understanding are quite different. Our paper explores the potential of transferring 2D model architectures and weights to understand 3D point-clouds, by empirically investigating the feasibility of the transfer, the benefits of the transfer, and shedding light on why the transfer works. We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds. Specifically, we transfer the image-pretrained model to a point-cloud model by copying or inflating the weights. We find that finetuning the transformed image-pretrained models (FIP) with minimal efforts --- only on input, output, and normalization layers --- can achieve competitive performance on 3D point-cloud classification, beating a wide range of point-cloud models that adopt task-specific architectures and use a variety of tricks. When finetuning the whole model, the performance gets further improved. Meanwhile, FIP improves data efficiency, reaching up to 10.0 top-1 accuracy percent on few-shot classification. It also speeds up training of point-cloud models by up to 11.1x for a target accuracy (e.g., 90 % accuracy). Lastly, we provide an explanation of the image to point-cloud transfer from the aspect of neural collapse."
FAR: Fourier Aerial Video Recognition
Authors: Divya Kothandaraman, Tianrui Guan, Xijun Wang, Shuowen Hu, Ming Lin, Dinesh Manocha
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We present a method, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background. Our disentanglement technique operates in the frequency domain to characterize the extent of temporal change of spatial pixels, and exploits convolution-multiplication properties of Fourier transform to map this representation to the corresponding object-background entangled features obtained from the network. To encapsulate contextual information and long-range space-time dependencies, we present a novel Fourier Attention algorithm, which emulates the benefits of self-attention by modeling the weighted outer product in the frequency domain. Our Fourier attention formulation uses much fewer computations than self-attention. We have evaluated our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night, Drone Action, and NEC Drone. We demonstrate a relative improvement of 8.02% -38.69% in top-1 accuracy over prior work."
Translating a Visual LEGO Manual to a Machine-Executable Plan
Authors: Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng, Jiajun Wu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers their 3D poses. This task poses the challenge of establishing a 2D-3D correspondence between the manual image and the real 3D object, and 3D pose estimation for unseen 3D objects, since a new component to be added in a step can be an object built from previous steps. To address these two challenges, we present a novel learning-based framework, the Manual-to-Executable-Plan Network (MEPNet), which reconstructs the assembly steps from a sequence of manual images. The key idea is to integrate neural 2D keypoint detection modules and 2D-3D projection algorithms for high-precision prediction and strong generalization to unseen components. The MEPNet outperforms existing methods on three newly collected LEGO manual datasets and a Minecraft house dataset."
Fabric Material Recovery from Video Using Multi-Scale Geometric Auto-Encoder
Authors: Junbang Liang, Ming Lin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Fabric materials are central to recreating realistic appearance of avatars in a virtual world and many VR applications, ranging from virtual try-on, teleconferencing, to character animation. We propose an end-to-end network model that uses video input to estimate the fabric materials of the garment worn by a human or an avatar in a virtual world. To achieve the high accuracy, we jointly learn human body and the garment geometry as conditions to material prediction. Due to the highly dynamic and deformable nature of cloth, general data-driven garment modeling remains a challenge. To address this problem, we propose a two-level auto-encoder to account for both global and local features of any garment geometry that would directly affect material perception. Using this network, we can also achieve smooth geometry transitioning between different garment topologies. During the estimation, we use a closed-loop optimization structure to share information between tasks and feed the learned garment features for temporal estimation of garment materials. Experiments show that our proposed network structures greatly improve the material classification accuracy by 1.5x, with applicability to unseen input. It also runs at least three orders of magnitude faster than the state-of-the-art. We demonstrate the recovered fabric materials on virtual try-on, where we recreate the entire avatar appearance, including body shape and pose, garment geometry and materials from only a single video."
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
Authors: Jie Ren, Wenteng Liang, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Large-scale Bundle Adjustment (BA) requires massive memory and computation resources which are difficult to be fulfilled by existing BA libraries. In this paper, we propose MegBA, a GPU-based distributed BA library. MegBA can provide massive aggregated memory by automatically partitioning large BA problems, and assigning the solvers of sub-problems to parallel nodes. The parallel solvers adopt distributed Precondition Conjugate Gradient and distributed Schur Elimination, so that an effective solution, which can match the precision of those computed by a single node, can be efficiently computed. To accelerate BA computation, we implement end-to-end BA computation using high-performance primitives available on commodity GPUs. MegBA exposes easy-to-use APIs that are compatible with existing popular BA libraries. Experiments show that MegBA can significantly outperform state-of-the-art BA libraries: Ceres (41.45×), RootBA (64.576×) and DeepLM (6.769×) in several large-scale BA benchmarks."
The One Where They Reconstructed 3D Humans and Environments in TV Shows
Authors: Georgios Pavlakos, Ethan Weber, Matthew Tancik, Angjoo Kanazawa
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of the existing work focuses on 2D recognition tasks. In this paper, we make the observation that there is a certain persistence in TV shows, i.e., repetition of the environments and the humans, which makes possible the 3D reconstruction of this content. Building on this insight, we propose an automatic approach that operates on an entire season of a TV show and aggregates information in 3D; we build a 3D model of the environment, compute camera information, static 3D scene structure and body scale information. Then, we demonstrate how this information acts as rich 3D context that can guide and improve the recovery of 3D human pose and position in these environments. Moreover, we show that reasoning about humans and their environment in 3D enables a broad range of downstream applications: re-identification, gaze estimation, cinematography and image editing. We apply our approach on environments from seven iconic TV shows and perform an extensive evaluation of the proposed system."
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information
Authors: Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang, Rishabh Iyer
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc. It is known that their success depends on a large amount of data from the domain of interest. While deep models often perform well in terms of overall accuracy, they often struggle in performance on rare yet critical data slices. For example, data slices like "motorcycle at night" or "bicycle at night" are often rare but very critical slices for self-driving applications and false negatives on such rare slices could result in ill-fated failures and accidents. Active learning (AL) is a well-known paradigm to incrementally and adaptively build training datasets with a human in the loop. However, current AL based acquisition functions are not well-equipped to tackle real-world datasets with rare slices, since they are based on uncertainty scores or global descriptors of the image. We propose TALISMAN, a novel framework for Targeted Active Learning or object detectIon with rare slices using Submodular MutuAl iNformation. Our method uses the submodular mutual information functions instantiated using features of the region of interest (RoI) to efficiently target and acquire data points with rare slices. We evaluate our framework on the standard PASCAL VOC07+12 and BDD100K, a real-world self-driving dataset. We observe that TALISMAN outperforms other methods by in terms of average precision on rare slices, and in terms of mAP.
An Efficient Person Clustering Algorithm for Open Checkout-Free Groceries
Authors: Junde Wu, Yu Zhang, Rao Fu, Yuanpei Liu, Jing Gao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Open checkout-free grocery is the grocery store where the customers never have to wait in line to check out. Developing a system like this is not trivial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for the system. Motivated by unique challenges in the open checkout-free grocery, we propose an efficient and effective person clustering method. Specifically, we first propose a Crowded Sub-Graph (CSG) to localize the relationship among massive and continuous data streams. CSG is constructed by the proposed Pick-Link-Weight (PLW) strategy, which picks the nodes based on time-space information, links the nodes via trajectory information, and weighs the links by the proposed von Mises-Fisher (vMF) similarity metric. Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG. GCN is adopted to project the features into low-dimensional separable space, and NN is able to quickly produce a result in this space upon dynamic person flow. The experimental results show that the proposed method outperforms other alternative algorithms in this scenario. In practice, the whole system has been implemented and deployed in several real-world open checkout-free groceries."
POP: Mining POtential Performance of New Fashion Products via Webly Cross-Modal Query Expansion
Authors: Christian Joppi, Geri Skenderi, Marco Cristani
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e., predicting the performance of a brand-new clothing probe with no available past observations. Our pipeline manufactures the missing past starting from a single, available image of the clothing probe. It starts by expanding textual tags associated with the image, querying related fashionable or unfashionable images uploaded on the web at a specific time in the past. A binary classifier is robustly trained on these web images by confident learning, to learn what was fashionable in the past and how much the probe image conforms to this notion of fashionability. This compliance produces the POtential Performance (POP) time series, indicating how performing the probe could have been if it were available earlier. POP proves to be highly predictive for the probe’s future performance, ameliorating the sales forecasts of all state-of-the-art models on the recent VISUELLE fast-fashion dataset. We also show that POP reflects the ground-truth popularity of new styles (ensembles of clothing items) on the Fashion Forward benchmark, demonstrating that our webly-learned signal is a truthful expression of popularity, accessible by everyone and generalizable to any time of analysis. Forecasting code, data and the POP time series are available at: https://github.com/HumaticsLAB/POP-Mining-POtential-Performance"
Pose Forecasting in Industrial Human-Robot Collaboration
Authors: Alessio Sampieri, Guido Maria D’Amely di Melendugno, Andrea Avogaro, Federico Cunico, Francesco Setti, Geri Skenderi, Marco Cristani, Fabio Galasso
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is 4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64."
Actor-Centered Representations for Action Localization in Streaming Videos
Authors: Sathyanarayanan Aakur, Sudeep Sarkar
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Event perception tasks such as recognizing and localizing actions in streaming videos are essential for scaling to real-world application contexts. We tackle the problem of learning actor-centered representations through the notion of continual hierarchical predictive learning to localize actions in streaming videos without the need for training labels and outlines for the objects in the video. We propose a framework driven by the notion of hierarchical predictive learning to construct actor-centered features by attention-based contextualization. The key idea is that predictable features or objects do not attract attention and hence do not contribute to the action of interest. Experiments on three benchmark datasets show that the approach can learn robust representations for localizing actions using only one epoch of training, i.e., a single pass through the streaming video. We show that the proposed approach outperforms unsupervised and weakly supervised baselines while offering competitive performance to fully supervised approaches. Additionally, we extend the model to multi-actor settings to recognize group activities while localizing the multiple, plausible actors. We also show that it generalizes to out-of-domain data with limited performance degradation."
Bandwidth-Aware Adaptive Codec for DNN Inference Offloading in IoT
Authors: Xiufeng Xie, Ning Zhou, Wentao Zhu, Ji Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The lightweight nature of IoT devices makes it challenging to run deep neural networks (DNNs) locally for applications like augmented reality. Recent advances in IoT communication like LTE-M have significantly boosted the link bandwidth, enabling IoT devices to stream visual data to edge servers running DNNs for inference. However, uncompressed visual data can still easily overload the IoT link, and the wireless spectrum is shared by numerous IoT devices, causing unstable link bandwidth. Mainstream codecs can reduce the traffic but at the cost of severe inference accuracy drops. Recent works on differentiable JPEG train the codec to tackle the damage to inference accuracy. But they rely on heuristic configurations in the loss function to balance the rate-accuracy tradeoff, providing no guarantee to meet the IoT bandwidth constraint. This paper presents AutoJPEG, a bandwidth-aware adaptive compression solution that learns the JPEG encoding parameters to optimize the DNN inference accuracy under bandwidth constraints. We model the compressed image size as a closed-form function of encoding parameters by analyzing the JPEG codec workflow. Furthermore, we formulate a constrained optimization framework to minimize the original DNN loss while ensuring the image size strictly meets the bandwidth constraint. Our evaluation validates AutoJPEG on various DNN models and datasets. In our experiments, AutoJPEG outperforms the mainstream codecs (like JPEG and WebP) and the state-of-the-art solutions that optimize the image codec for DNN inference."
Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
Authors: Paritosh Parmar, Amol Gharat, Helge Rhodin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. Detecting errors in workout form naturally requires estimating human’s body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in the workouts are very subtle. To that end, we propose to learn exercise-oriented image and video representations from unlabeled samples such that a small dataset annotated by experts suffices for supervised error detection. In particular, our domain knowledge-informed self-supervised approaches (pose contrastive learning and motion disentangling) exploit the harmonic motion of the exercise actions, and capitalize on the large variances in camera angles, clothes, and illumination to learn powerful representations. To facilitate our self-supervised pretraining, and supervised finetuning, we curated a new exercise dataset, Fitness-AQA (https://github.com/ParitoshParmar/Fitness-AQA), comprising of three exercises: BackSquat, BarbellRow, and OverheadPress. It has been annotated by expert trainers for multiple crucial and typically occurring exercise errors. Experimental results show that our self-supervised representations outperform off-the-shelf 2D- and 3D-pose estimators and several other baselines. We also show that our approaches can be applied to other domains/tasks such as pose estimation and dive quality assessment."
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Authors: Mohan Zhou, Yalong Bai, Wei Zhang, Ting Yao, Tiejun Zhao, Tao Mei
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensable complement to talking heads generation, listening head generation has seldomly been studied in literature. Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots. In this work, we propose a novel dataset "ViCo", highlighting the listening head generation during a face-to-face conversation. A total number of 92 identities (67 speakers and 76 listeners) are involved in ViCo, featuring 483 clips in a paired "speaking-listening" pattern, where listeners show three listening styles based on their attitudes: positive, neutral, negative. Different from traditional speech-to-gesture or talking-head generation, listening head generation takes as input both the audio and visual signals from the speaker, and gives non-verbal feedbacks (e.g., head motions, facial expressions) in a real-time manner. Our dataset supports a wide range of applications such as human-to-human interaction, video-to-video translation, cross-modal understanding and generation. To encourage further research, we also release a listening head generation baseline, conditioning on different listening attitudes. Code & ViCo dataset: https://project.mhzhou.com/vico.
"Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics"
Authors: Sen Zhang, Jing Zhang, Dacheng Tao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years. Although current methods have reached a high up-to-scale accuracy, they usually fail to learn the true scale metric due to the inherent scale ambiguity from training with monocular sequences. In this work, we tackle this problem and propose DynaDepth, a novel scale-aware framework that integrates information from vision and IMU motion dynamics. Specifically, we first propose an IMU photometric loss and a cross-sensor photometric consistency loss to provide dense supervision and absolute scales. To fully exploit the complementary information from both sensors, we further drive a differentiable camera-centric extended Kalman filter (EKF) to update the IMU preintegrated motions when observing visual measurements. In addition, the EKF formulation enables learning an ego-motion uncertainty measure, which is non-trivial for unsupervised methods. By leveraging IMU during training, DynaDepth not only learns an absolute scale, but also provides a better generalization ability and robustness against vision degradation such as illumination change and moving objects. We validate the effectiveness of DynaDepth by conducting extensive experiments and simulations on the KITTI and Make3D datasets."
TIPS: Text-Induced Pose Synthesis
Authors: Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, Umapada Pal, Michael Blumenstein
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments."
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Authors: Haolin Yuan, Bo Hui, Yuchen Yang, Philippe Burlina, Neil Zhenqiang Gong, Yinzhi Cao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs from one client to another. Existing personalized FL algorithms are only applicable to narrow cases, e.g., one or two data classes per client, and therefore they do not satisfactorily address FL under varying levels of data heterogeneity. In this paper, we propose a novel framework, called DisTrans, to improve FL performance (i.e., model accuracy) via train and test-time distributional transformations along with a double-input-channel model structure. DisTrans works by optimizing distributional offsets and models for each FL client to shift their data distribution, and aggregates these offsets at the FL server to further improve performance in case of distributional heterogeneity. Our evaluation on multiple benchmark datasets shows that DisTrans outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity (e.g., for CelebA and 100% heterogeneity DisTrans has accuracy of 80.4% vs. 72.1% or lower for other SOTA approaches)."
Where in the World Is This Image? Transformer-Based Geo-Localization in the Wild
Authors: Shraman Pramanick, Ewa M. Nowara, Joshua Gleason, Carlos D. Castillo, Rama Chellappa
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time of the day, weather, season, and more importantly, the prediction is made from a single image possibly having only a few geo-locating cues. For these reasons, most existing works are restricted to specific cities, imagery, or worldwide landmarks. In this work, we focus on developing an efficient solution to planet-scale single-image geo-localization. To this end, we propose TransLocator, a unified dual-branch transformer network that attends to tiny details over the entire image and produces robust feature representation under extreme appearance variations. TransLocator takes an RGB image and its semantic segmentation map as inputs, interacts between its two parallel branches after each transformer layer, and simultaneously performs geo-localization and scene recognition in a multi-task fashion. We evaluate TransLocator on four benchmark datasets - Im2GPS, Im2GPS3k, YFCC4k, YFCC26k and obtain 5.5%, 14.1%, 4.9%, 9.9% continent-level accuracy improvement over the state-of-the-art. TransLocator is also validated on real-world test images and found to be more effective than previous methods."
Colorization for In Situ Marine Plankton Images
Authors: Guannan Guo, Qi Lin, Tao Chen, Zhenghui Feng, Zheng Wang, Jianping Li
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Underwater imaging with red-NIR light illumination can avoid phototropic aggregation-induced observational deviation of marine plankton abundance under white light illumination, but this will lead to the loss of critical color information in the collected grayscale images, which is non-preferable to subsequent human and machine recognition. We present a novel deep networks-based vision system IsPlanktonCLR for automatic colorization of in situ marine plankton images. IsPlanktonCLR uses a reference module to generate self-guidance from a customized palette, which is obtained by clustering in situ plankton image colors. With this self-guidance, a parallel colorization module restores input grayscale images into their true color counterparts. Additionally, a new metric for image colorization evaluation is proposed, which can objectively reflect the color dissimilarity between comparative images. Experiments and comparisons with state-of-the-art approaches are presented to show that our method achieves a substantial improvement over previous methods on color restoration of scientific plankton image data."
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection
Authors: Mingyu Yang, Yu Chen, Hun-Seok Kim
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In recent years, deep learning-based approaches for visual-inertial odometry (VIO) have shown remarkable performance outperforming traditional geometric methods. Yet, all existing methods use both the visual and inertial measurements for every pose estimation incurring potential computational redundancy. While visual data processing is much more expensive than that for the inertial measurement unit (IMU), it may not always contribute to improving the pose estimation accuracy. In this paper, we propose an adaptive deep-learning based VIO method that reduces computational redundancy by opportunistically disabling the visual modality. Specifically, we train a policy network that learns to deactivate the visual feature extractor on the fly based on the current motion state and IMU readings. A Gumbel-Softmax trick is adopted to train the policy network to make the decision process differentiable for end-to-end system training. The learned strategy is interpretable, and it shows scenario-dependent decision patterns for adaptive complexity reduction. Experiment results show that our method achieves a similar or even better performance than the full-modality baseline with up to 78.8% computational complexity reduction for KITTI dataset evaluation. The code is available at https://github.com/mingyuyng/Visual-Selective-VIO."
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch
Authors: Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang, James Hays
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We address the problem of retrieving in-the-wild images with both a sketch and a text query. We present TASK-former (Text And SKetch transformer), an end-to-end trainable model for image retrieval using a text description and a sketch as input. We argue that both input modalities complement each other in a manner that cannot be achieved easily by either one alone. TASK-former follows the late-fusion dual-encoder approach, similar to CLIP, which allows efficient and scalable retrieval since the retrieval set can be indexed independently of the queries. We empirically demonstrate that using an input sketch (even a poorly drawn one) in addition to text considerably increases retrieval recall compared to traditional text-based image retrieval. To evaluate our approach, we collect 5,000 hand-drawn sketches for images in the test set of the COCO dataset. The collected sketches are available a https://janesjanes.github.io/tsbir/."
A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D
Authors: Tianyi Liu, Sen He, Vinodh Kumaran Jayakumar, Wei Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "In Cloud 3D, such as Cloud Gaming and Cloud Virtual Reality (VR), image frames are rendered and compressed (encoded) in the cloud, and sent to the clients for users to view. For low latency and high image quality, fast, high compression rate, and high-quality image compression techniques are preferable. This paper explores computation time reduction techniques for learned image compression to make it more suitable for cloud 3D. More specifically, we employed slim (low-complexity) and application-specific AI models to reduce the computation time without degrading image quality. Our approach is based on two key insights: (1) as the frames generated by a 3D application are highly homogeneous, application-specific compression models can improve the rate-distortion performance over a general model; (2) many computer-generated frames from 3D applications are less complex than natural photos, which makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining about 5x to 9x speedup and reducing the compression time to be less than 1 second (0.74s), bringing learned image compression closer to being viable for cloud 3D. Code is available at https://github.com/cloud-graphics-rendering/AppSpecificLIC."
AutoTransition: Learning to Recommend Video Transition Effects
Authors: Yaojie Shen, Libo Zhang, Kai Xu, Xiaojie Jin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Video transition effects are widely used in video editing to connect shots for creating cohesive and visually appealing videos. However, it is challenging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on performing automatic video transitions recommendation (VTR): given a sequence of raw video shots and companion audio, recommend video transitions for each pair of neighboring shots. To solve this task, we collect a large-scale video transition dataset using publicly available video templates on editing softwares. Then we formulate VTR as a multi-modal retrieval problem from vision/audio to video transitions and propose a novel multi-modal matching framework which consists of two parts. First we learn the embedding of video transitions through a video transition classification task. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both quantitative and qualitative experiments, we clearly demonstrate the effectiveness of our method. Notably, in the comprehensive user study, our method receives comparable scores compared with professional editors while improving the video editing efficiency by 300x. We hope our work serves to inspire other researchers to work on this new task. The dataset and codes are public at https://github.com/acherstyx/AutoTransition."
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
Authors: Romain Loiseau, Mathieu Aubry, Loïc Landrieu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on 360Â° frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a 10 billion point dataset with fine-grained labels, timestamps, and sensor rotation information necessary to accurately assess the real-time readiness of segmentation algorithms. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR sequences. Helix4D operates on acquisition slices corresponding to a fraction of a full sensor rotation, significantly reducing the total latency. Helix4D reaches accuracy on par with the best segmentation algorithms on HelixNet and SemanticKITTI with a reduction of over 5x in terms of latency and 50x in model size. The code and data are available at: https://romainloiseau.fr/helixnet"
Open-World Semantic Segmentation for LIDAR Point Clouds
Authors: Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai, Di Luan, Mingqian Tang, Ming Liu, Michael Yu Wang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Classical LIDAR semantic segmentation is not robust for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set network is only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, we propose the open-world semantic segmentation task for LIDAR point clouds, which aims to 1) identify both old and novel classes using open-set semantic segmentation, and 2) gradually incorporate novel objects into the existing knowledge base using incremental learning without forgetting old classes. We propose a REdundAncy cLassifier (REAL) framework to provide a general architecture for both open-set semantic segmentation and incremental learning. The experimental results show that REAL can achieves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting with a large margin during incremental learning."