GenAI4ELab/papercli-papers huggingface dataset:
Sketch-Guided Object Localization in Natural Images
Authors: Aditay Tripathi; Rajath R. Dani; Anand Mishra and Anirban Chakraborty
Venue: ECCV 2020 (main)

Abstract: We introduce a novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch-guided object localization. This problem is distinctively different from the traditional sketch-based image retrieval task where the gallery set often contains images with only one object. The sketch-guided object localization proves to be more challenging when we consider the following: (i) the sketches used as queries are abstract representations with little information on the shape and salient attributes of the object, (ii) the sketches have significant variability as they are hand-drawn by a diverse set of untrained human subjects, and (iii) there exists a domain gap between sketch queries and target natural images as these are sampled from very different data distributions. To address the problem of sketch-guided object localization, we propose a novel mph{cross-modal attention} scheme that guides the region proposal network (RPN) to generate object proposals relevant to the sketch query. These object proposals are later scored against the query to obtain final localization. Our method is effective with as little as a single sketch query. Moreover, it also generalizes well to object categories not seen during training and is effective in localizing multiple object instances present in the image. Furthermore, we extend our framework to a multi-query setting using novel feature fusion and attention fusion strategies introduced in this paper. The localization performance is evaluated on publicly available object detection benchmarks, viz. MS-COCO and PASCAL-VOC, with sketch queries obtained from `Quick, Draw!'. The proposed method significantly outperforms related baselines on both single-query and multi-query localization tasks."
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Authors: Malik Boudiaf; Jérôme Rony; Imtiaz Masud Ziko; Eric Granger; Marco Pedersoli; Pablo Piantanida; Ismail Ben Ayed
Venue: ECCV 2020 (main)

Abstract: Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing complex pairwise-distance losses, which require convoluted schemes to ease optimization, such as sample mining or pair weighting. The standard cross-entropy loss for classification has been largely overlooked in DML. On the surface, the cross-entropy may seem unrelated and irrelevant to metric learning as it does not explicitly involve pairwise distances. However, we provide a theoretical analysis that links the cross-entropy to several well-known and recent pairwise losses. Our connections are drawn from two different perspectives: one based on an explicit optimization insight; the other on discriminative and generative views of the mutual information between the labels and the learned features. First, we explicitly demonstrate that the cross-entropy is an upper bound on a new pairwise loss, which has a structure similar to various pairwise losses: it minimizes intra-class distances while maximizing inter-class distances. As a result, minimizing the cross-entropy can be seen as an approximate bound-optimization (or Majorize-Minimize) algorithm for minimizing this pairwise loss. Second, we show that, more generally,minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses. Furthermore, we show that various standard pairwise losses can be explicitly related to one another via bound relationships. Our findings indicate that the cross-entropy represents a proxy for maximizing the mutual information – as pairwise losses do – without the need for convoluted sample-mining heuristics. Our experiments†over four standard DML benchmarks strongly support our findings. We obtain state-of-the-art results, outperforming recent and complex DML methods."
Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
Authors: Jize Cao; Zhe Gan; Yu Cheng; Licheng Yu; Yen-Chun Chen; Jingjing Liu
Venue: ECCV 2020 (main)

Abstract: Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. Models such as ViLBERT, LXMERT and UNITER have significantly lifted state of the art across a wide range of V+L benchmarks. However, little is known about the inner mechanisms that destine their impressive success. To reveal the secrets behind the scene, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e.g., Visual Coreference Resolution, Visual Relation Detection) generalizable to standard pre-trained V+L models, to decipher the inner workings of multimodal pre-training (e.g., implicit knowledge garnered in individual attention heads, inherent cross-modal alignment learned through contextualized multimodal embeddings). Through extensive analysis of each archetypal model architecture via these probing tasks, our key observations are: (i) Pre-trained models exhibit a propensity for attending over text rather than images during inference. (ii) There exists a subset of attention heads that are tailored for capturing cross-modal interactions. (iii) Learned attention matrix in pre-trained models demonstrates patterns coherent with the latent alignment between image regions and textual words. (iv) Plotted attention patterns reveal visually-interpretable relations among image regions. (v) Pure linguistic knowledge is also effectively encoded in the attention heads. These are valuable insights serving to guide future work towards designing better model architecture and objectives for multimodal pre-training."
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement
Authors: William Peebles; John Peebles; Jun-Yan Zhu; Alexei Efros; Antonio Torralba
Venue: ECCV 2020 (main)

Abstract: Existing popular methods for disentanglement rely on hand-picked priors and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a simple regularization function that encourages the input Hessian of a function to be diagonal. Our method is completely model-agnostic and can be applied to any deep generator with just a few lines of code. We show that our method automatically uncovers meaningful factors of variation in the standard basis when applied to ProgressiveGAN across several datasets. Additionally, we demonstrate that our regularization term can be used to identify interpretable directions in BigGAN's latent space in a fully unsupervised fashion. Finally, we provide provide empirical evidence that our regularization term encourages sparsity when applied to overparameterized latent spaces."
STAR: Sparse Trained Articulated Human Body Regressor
Authors: Ahmed A. A. Osman; Timo Bolkart; Michael J. Black
Venue: ECCV 2020 (main)

Abstract: The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape. While popular, we show that SMPL has several limitations and introduce STAR, which is quantitatively and qualitatively superior to SMPL. First, SMPL has a huge number of parameters resulting from its use of global blend shapes. These dense pose-corrective offsets relate every vertex on the mesh to all the joints in the kinematic tree, capturing spurious long-range cor- relations. To address this, we define per-joint pose correctives and learn the subset of mesh vertices that are influenced by each joint movement. This sparse formulation results in more realistic deformations and significantly reduces the number of model parameters to 20% of SMPL. When trained on the same data as SMPL, STAR generalizes better despite having many fewer parameters. Second, SMPL factors pose-dependent deformations from body shape while, in reality, people with different shapes deform differently. Consequently, we learn shape-dependent pose- corrective blendshapes that depend on both body pose and BMI. Third, we show that the shape space of SMPL is not rich enough to capture the variation in the human population. We address this by training STAR with an additional 10,000 scans of male and female subjects, and show that this results in better model generalization. STAR is compact, generalizes better to new bodies and is a drop-in replacement for SMPL. STAR is publicly available for research purposes at http://star.is.tue.mpg.de."
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
Authors: Xinghao Chen; Yiman Zhang; Yunhe Wang; Han Shu; Chunjing Xu; Chang Xu
Venue: ECCV 2020 (main)

Abstract: Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer stability across frames, optical flow is widely adopted, despite its high computational complexity, e.g., occupying over 97% inference time. This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm. We adopt two teacher networks, one of which takes optical flow during inference while the other does not. The output difference between these two teacher networks highlights the improvements made by optical flow, which is then adopted to distill the target student network. Furthermore, a low-rank distillation loss is employed to stabilize the output of student network by mimicking the rank of input videos. Extensive experiments demonstrate that our student network without an optical flow module is still able to generate stable video and runs much faster than the teacher network."
Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning
Authors: Sihui Luo; Wenwen Pan; Xinchao Wang; Dazhou Wang; Haihong Tang; Mingli Song
Venue: ECCV 2020 (main)

Abstract: A vast number of well-trained deep networks have been released by developers online for plug-and-play use. These networks specialize in different tasks and in many cases, the data and annotations used to train them are not publicly available. In this paper, we study how to reuse such heterogeneous pre-trained models as teachers, and build a versatile and compact student model, without accessing human annotations. To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model. This is achieved via a dual-step adaptive competitive-cooperation training approach, where the knowledge of the heterogeneous teachers are in the first step amalgamated to guide the shared parameter learning of the student network, and followed by a gradient-based competition-balancing strategy to learn the multi-head prediction network as well as the loss weightings of the distinct tasks in the second step. The two steps, which we term as the collaboration and competition step respectively, are performed alternatively until the balance of the competition is reached for the ultimate collaboration. Experimental results demonstrate that, the learned student not only comes with a smaller size but all achieves performances on par with or even superior to those of the teachers."
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
Authors: Shizhen Zhao; Changxin Gao; Jun Zhang; Hao Cheng; Chuchu Han; Xinyang Jiang; Xiaowei Guo; Wei-Shi Zheng; Nong Sang; Xing Sun
Venue: ECCV 2020 (main)

Abstract: In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods."
Learning 3D Part Assembly from a Single Image
Authors: Yichen Li; Kaichun Mo; Lin Shao; Minhyuk Sung; Leonidas Guibas
Venue: ECCV 2020 (main)

Abstract: Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have been extensively studied in robotics. However, when it comes to task specification, the space of possibilities remains under-explored. Towards this end, we introduce a novel problem,single-image-guided 3D part assembly, along with a learning-based solution. We study this problem in the setting of furniture assembly from a given complete set of parts and a single image depicting the entire assembled object. Multiple challenges exist in this setting, including handling ambiguity among parts (e.g., slats in a chair back and leg stretchers) and 3D pose prediction for parts and part subassemblies,whether visible or occluded. We address these issues by proposing a two-module pipeline that leverages strong 2D-3D correspondences and assembly-oriented graph message-passing to infer part relationships. In experiments with a PartNet-based synthetic benchmark, we demonstrate the effectiveness of our framework as compared with three baseline approaches."
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
Authors: Kaichun Mo; He Wang; Xinchen Yan; Leonidas Guibas
Venue: ECCV 2020 (main)

Abstract: Generative 3D shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating a 3D point cloud geometry for a shape from a symbolic part tree representation. In order to learn such a conditional shape generation procedure in an end-to-end fashion, we propose a conditional GAN “part tree”-to-“point cloud” model (PT2PC) that disentangles the structural and geometric factors. The proposed model incorporates the part tree condition into the architecture design by passing messages top-down and bottom-up along the part tree hierarchy. Experimental results and user study demonstrate the strengths of our method in generating perceptually plausible and diverse 3D point clouds, given the part tree condition. We also propose a novel structural measure for evaluating if the generated shape point clouds satisfy the part tree conditions."
Highly Efficient Salient Object Detection with 100K Parameters
Authors: Shang-Hua Gao; Yong-Qiang Tan; Ming-Ming Cheng; Chengze Lu; Yunpeng Chen; Shuicheng Yan
Venue: ECCV 2020 (main)

Abstract: Salient object detection models often demand a considerable amount of computation cost to make precise prediction for each pixel, making them hardly applicable on low-power devices. In this paper, we aim to relieve the contradiction between computation cost and model performance by improving the network efficiency to a higher degree. We propose a flexible convolutional module, namely generalized OctConv (gOctConv), to efficiently utilize both in-stage and cross-stages multi-scale features, while reducing the representation redundancy by a novel dynamic weight decay scheme. The effective dynamic weight decay scheme stably boosts the sparsity of parameters during training, supports learnable number of channels for each scale in gOctConv, allowing 80% of parameters reduce with negligible performance drop. Utilizing gOctConv, we build an extremely light-weighted model, namely CSNet, which achieves comparable performance with about 0.2% parameters (100k) of large models on popular salient object detection benchmarks. The source code is publicly available at https://mmcheng.net/sod100k."
HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing
Authors: Qili Deng; Ziling Huang; Chung-Chi Tsai; Chia-Wen Lin
Venue: ECCV 2020 (main)

Abstract: In this paper, we present a Haze-Aware Representation Distillation Generative Adversarial Network named HardGAN for single-image dehazing. Unlike previous studies that intend to model the transmission map and global atmospheric light jointly to restore a clear image, we solve this regression problem by a multi-scale structure neural network embedded with our proposed Haze-Aware Representation Distillation (HARD) layer. Moreover, we re-introduce to utilize the normalization layer skillfully instead of stacking with the convolution layer directly as before to avoid the useful information wash away, as claimed in many image quality enhancement studies. Extensive experiment on several synthetic benchmark datasets as well as the NTIRE 2020 real-world images show our proposed multi-layer GAN-based network with HARD performs favorably against the state-of-the-art methods in terms of PSNR, SSIM, LPIPS, and human subjective evaluation."
Lifespan Age Transformation Synthesis
Authors: Roy Or-El; Soumyadip Sengupta; Ohad Fried; Eli Shechtman; Ira Kemelmacher-Shlizerman
Venue: ECCV 2020 (main)

Abstract: We address the problem of single photo age progression and regression---the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. We propose a new multi domain image-to-image generative adversarial network architecture, whose learned latent space accurately models the continuous aging process in both directions. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation, where fixed age classes are used as anchors to approximate the continuous age transformation. Our framework can predict a full head portrait in ages 0-70 from a single photo, modifying both texture and shape of the head. We demonstrate results on a wide variety of photos and datasets, and show significant improvement over the state of the art."
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
Authors: Xingchao Peng; Yichen Li; Kate Saenko
Venue: ECCV 2020 (main)

Abstract: Conventional unsupervised domain adaptation (UDA) studies the knowledge transfer between a limited number of domains. This neglects the more practical scenario where data are distributed in numerous different domains in the real world. The domain similarity between those domains is critical for domain adaptation performance. To describe and learn relations between different domains, we propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix. To evaluate the effectiveness of our Domain2Vec model, we create two large-scale cross-domain benchmarks. The first one is TinyDA, which contains 54 domains and about one million MNIST-style images. The second benchmark is DomainBank, which is collected from 56 existing vision datasets. We demonstrate that our embedding is capable of predicting domain similarities that match our intuition about visual relations between different domains. Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. "
Simulating Content Consistent Vehicle Datasets with Attribute Descent
Authors: Yue Yao; Liang Zheng; Xiaodong Yang; Milind Naphade; Tom Gedeon
Venue: ECCV 2020 (main)

Abstract: This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the latter has been widely studied, we focus on reducing the content gap in attributes like illumination and viewpoint. To reduce the problem complexity, we choose a smaller and more controllable application, vehicle re-identification (re-ID). We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets. Specifically, we manipulate each attribute in VehicleX, aiming to minimize the discrepancy between VehicleX and real data in terms of the Fr ́echet Inception Distance (FID). This attribute descent algorithm allows content domain adaptation (DA) orthogonal to existing appearance DA methods. We mix the optimized VehicleX data with real-world vehicle re-ID datasets, and observe consistent improvement. With the augmented datasets, we report competitive accuracy. We make the dataset, engine and our codes available at https://github.com/yorkeyao/VehicleX/."
Multiview Detection with Feature Perspective Transformation
Authors: Yunzhong Hou; Liang Zheng; Stephen Gould
Venue: ECCV 2020 (main)

Abstract: Incorporating multiple camera views for detection alleviates the impact of occlusions in crowded scenes. In a multiview detection system, we need to answer two important questions. First, how should we aggregate cues from multiple views? Second, how should we aggregate information from spatially neighboring locations? To address these questions, we introduce a novel multiview detector, MVDet. During multiview aggregation, for each location on the ground, existing methods use multiview anchor box features as representation, which potentially limits performance as pre-defined anchor boxes can be inaccurate. In contrast, via feature map perspective transformation, MVDet employs anchor-free representations with feature vectors directly sampled from corresponding pixels in multiple views. For spatial aggregation, different from previous methods that require design and operations outside of neural networks, MVDet takes a fully convolutional approach with large convolutional kernels on the multiview aggregated feature map. The proposed model is end-to-end learnable and achieves 88.2% MODA on Wildtrack dataset, outperforming the state-of-the-art by 14.1%. We also provide detailed analysis of MVDet on a newly introduced synthetic dataset, MultiviewX, which allows us to control the level of occlusion. Code and MultiviewX dataset are available at https://github.com/hou-yz/MVDet/."