GenAI4ELab/papercli-papers huggingface dataset:
Revisiting a kNN-based Image Classification System with High-capacity Storage
Authors: Kengo Nakata; Youyang Ng; Daisuke Miyashita; Asuka Maki; Yu-Chieh Lin; Jun Deguchi
Venue: ECCV 2022 (main)

Abstract: "In existing image classification systems that use deep neural networks, the knowledge needed for image classification is implicitly stored in model parameters. If users want to update this knowledge, then they need to fine-tune the model parameters. Moreover, users cannot verify the validity of inference results or evaluate the contribution of knowledge to the results. In this paper, we investigate a system that stores knowledge for image classification, such as image feature maps, labels, and original images, not in model parameters but in external storage. Our system refers to the storage like a database when classifying input images. To increase knowledge, our system updates the database instead of fine-tuning model parameters, which avoids catastrophic forgetting in incremental learning scenarios. We revisit a kNN (k-nearest neighbor) classifier and employ it in our system. By analyzing the neighborhood samples referred by the kNN algorithm, we can interpret how knowledge learned in the past is used for inference results. Our system achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset in the task incremental learning setting."
Geometric Representation Learning for Document Image Rectification
Authors: Hao Feng; Wengang Zhou; Jiajun Deng; Yuechen Wang; Houqiang Li
Venue: ECCV 2022 (main)

Abstract: "In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one. How- ever, such geometric constraints are largely ignored in existing advanced solutions, which limits the rectification performance. To this end, we present DocGeoNet for document image rectification by introducing explicit geometric representation. Technically, two typical attributes of the document image are involved in the proposed geometric representation learning, i.e., 3D shape and textlines. Our motivation raises from the insight that 3D shape provides global unwarping cues for rectifying a distorted document image, while overlooking the local structure. On the other hand, textlines complementarily provide explicit geometric constraints for local patterns. The learned geometric representation effectively bridges the distorted image and the ground truth one. Extensive experiments show the effectiveness of our framework and demonstrate the superiority of our DocGeoNet over state-of-the-art methods on both the DocUNet Benchmark dataset and our proposed DIR300 test set."
S2-VER: Semi-Supervised Visual Emotion Recognition
Authors: Guoli Jia; Jufeng Yang
Venue: ECCV 2022 (main)

Abstract: "Visual emotion recognition (VER), which plays an important role in various applications, has attracted increasing attention of researchers. Due to the ambiguous characteristic of emotion, it is hard to annotate a reliable large-scale dataset in this field. An alternative solution is semi-supervised learning (SSL), which progressively selects high-confidence samples from unlabeled data to help optimize the model. However, it is challenging to directly employ existing SSL algorithms in VER task. On the one hand, compared with object recognition, in VER task, the accuracy of the produced pseudo labels for unlabeled data drops a large margin. On the other hand, the maximum probability in the prediction is difficult to reach the fixed threshold, which leads to few unlabeled samples can be leveraged. Both of them would induce the suboptimal performance of the learned model. To address these issues, we propose S2-VER, the first SSL algorithm for VER, which consists of two com- ponents. The first component, reliable emotion label learning, aims to improve the accuracy of pseudo-labels. In detail, it generates smoothing labels by computing the similarity between the maintained emotion prototypes and the embedding of the sample. The second one is ambiguity-aware adaptive threshold strategy, which is dedicated to leveraging more unlabeled samples. Specifically, our strategy uses information entropy to measure the ambiguity of the smoothing labels, then adaptively adjusts the threshold, which is adopted to select high-confidence unlabeled samples. Extensive experiments conducted on six public datasets show that our proposed S2-VER performs favorably against the state-of-the-art approaches. The code is available at https://github.com/exped1230/S2-VER."
Image Coding for Machines with Omnipotent Feature Learning
Authors: Ruoyu Feng; Xin Jin; Zongyu Guo; Runsen Feng; Yixin Gao; Tianyu He; Zhizheng Zhang; Simeng Sun; Zhibo Chen
Venue: ECCV 2022 (main)

Abstract: "Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success. In this paper, we attempt to develop an ICM framework by learning universal features while also considering compression. We name such features as omnipotent features and the corresponding framework as Omni-ICM. Considering self-supervised learning (SSL) improves feature generalization, we integrate it with the compression task into the Omni-ICM framework to learn omnipotent features. However, it is non-trivial to coordinate semantics modeling in SSL and redundancy removing in compression, so we design a novel information filtering (IF) module between them by co-optimization of instance distinguishment and entropy minimization to adaptively drop information that is weakly related to AI tasks (e.g., some texture redundancy). Different from previous task-specific solutions, Omni-ICM could directly support AI tasks analysis based on the learned omnipotent features without joint training or extra transformation. Albeit simple and intuitive, Omni-ICM significantly outperforms existing traditional and learning-based codecs on multiple fundamental vision tasks."
Feature Representation Learning for Unsupervised Cross-Domain Image Retrieval
Authors: Conghui Hu; Gim Hee Lee
Venue: ECCV 2022 (main)

Abstract: "Current supervised cross-domain image retrieval methods can achieve excellent performance. However, the cost of data collection and labeling imposes an intractable barrier to practical deployment in real applications. In this paper, we investigate the unsupervised cross-domain image retrieval task, where class labels and pairing annotations are no longer a prerequisite for training. This is an extremely challenging task because there is no supervision for both in-domain feature representation learning and cross-domain alignment. We address both challenges by introducing: 1) a new cluster-wise contrastive learning mechanism to help extract class semantic-aware features, and 2) a novel distance-of-distance loss to effectively measure and minimize the domain discrepancy without any external supervision. Experiments on the Office-Home and DomainNet datasets consistently show the superior image retrieval accuracies of our framework over state-of-the-art approaches. Our source code can be found at https://github.com/conghuihu/UCDIR."
"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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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
Venue: ECCV 2022 (main)

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."