GenAI4ELab/papercli-papers huggingface dataset:
Improving the Reliability for Confidence Estimation
Authors: Haoxuan Qu; Yanchao Li; Lin Geng Foo; Jason Kuen; Jiuxiang Gu; Jun Liu
Venue: ECCV 2022 (main)

Abstract: "Confidence estimation, a task that aims to evaluate the trustworthiness of the model’s prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification."
Fine-Grained Scene Graph Generation with Data Transfer
Authors: Ao Zhang; Yuan Yao; Qianyu Chen; Wei Ji; Zhiyuan Liu; Maosong Sun; Tat-Seng Chua
Venue: ECCV 2022 (main)

Abstract: "Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By applying our proposed method, a Neural Motif model doubles the macro performance for informative SGG. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch."
Pose2Room: Understanding 3D Scenes from Human Activities
Authors: Yinyu Nie; Angela Dai; Xiaoguang Han; Matthias Nießner
Venue: ECCV 2022 (main)

Abstract: "With wearable IMU sensors, one can estimate human poses from wearable devices without requiring visual input. In this work, we pose the question: Can we reason about object structure in real-world environments solely from human trajectory information? Crucially, we observe that human motion and interactions tend to give strong information about the objects in a scene -- for instance a person sitting indicates the likely presence of a chair or sofa. To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment. P2R-Net models the probability distribution of object class as well as a deep Gaussian mixture model for object boxes, enabling sampling of multiple, diverse, likely modes of object configurations from an observed human trajectory. In our experiments we show that P2R-Net can effectively learn multi-modal distributions of likely objects for human motions, and produce a variety of plausible object structures of the environment, even without any visual information. The results demonstrate that P2R-Net consistently outperforms the baselines on the PROX dataset and the VirtualHome platform."
Towards Hard-Positive Query Mining for DETR-Based Human-Object Interaction Detection
Authors: Xubin Zhong; Changxing Ding; Zijian Li; Shaoli Huang
Venue: ECCV 2022 (main)

Abstract: "Human-Object Interaction (HOI) detection is a core task for high-level image understanding. Recently, Detection Transformer (DETR)-based HOI detectors have become popular due to their superior performance and efficient structure. However, these approaches typically adopt fixed HOI queries for all testing images, which is vulnerable to the location change of objects in one specific image. Accordingly, in this paper, we propose to enhance DETR’s robustness by mining hard-positive queries, which are forced to make correct predictions using partial visual cues. First, we explicitly compose hard-positive queries according to the ground-truth (GT) position of labeled human-object pairs for each training image. Specifically, we shift the GT bounding boxes of each labeled human-object pair so that the shifted boxes cover only a certain portion of the GT ones. We encode the coordinates of the shifted boxes for each labeled human-object pair into an HOI query. Second, we implicitly construct another set of hard-positive queries by masking the top scores in cross-attention maps of the decoder layers. The masked attention maps then only cover partial important cues for HOI predictions. Finally, an alternate strategy is proposed that efficiently combines both types of hard queries. In each iteration, both DETR’s learnable queries and one selected type of hard-positive queries are adopted for loss computation. Experimental results show that our proposed approach can be widely applied to existing DETR-based HOI detectors. Moreover, we consistently achieve state-of-the-art performance on three benchmarks: HICO-DET, V-COCO, and HOI-A."
Discovering Human-Object Interaction Concepts via Self-Compositional Learning
Authors: Zhi Hou; Baosheng Yu; Dacheng Tao
Venue: ECCV 2022 (main)

Abstract: "A comprehensive understanding of human-object interaction (HOI) requires detecting not only a small portion of predefined HOI concepts (or categories) but also other reasonable HOI concepts, while current approaches usually fail to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable combinations of verbs and objects). In this paper, 1) we introduce a novel and challenging task for a comprehensive HOI understanding, which is termed as HOI Concept Discovery; and 2) we devise a self-compositional learning framework (or SCL) for HOI concept discovery. Specifically, we maintain an online updated concept confidence matrix during training: 1) we assign pseudo-labels for all composite HOI instances according to the concept confidence matrix for self-training; and 2) we update the concept confidence matrix using the predictions of all composite HOI instances. Therefore, the proposed method enables the learning on both known and unknown HOI concepts. We perform extensive experiments on several popular HOI datasets to demonstrate the effectiveness of the proposed method for HOI concept discovery, object affordance recognition and HOI detection. For example, the proposed self-compositional learning framework significantly improves the performance of 1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO, respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively over 30% and 20%, respectively. Code is publicly available at https://github.com/zhihou7/HOI-CL."
Primitive-Based Shape Abstraction via Nonparametric Bayesian Inference
Authors: Yuwei Wu; Weixiao Liu; Sipu Ruan; Gregory S. Chirikjian
Venue: ECCV 2022 (main)

Abstract: "3D shape abstraction has drawn great interest over the years. Apart from low-level representations such as meshes and voxels, researchers also seek to semantically abstract complex objects with basic geometric primitives. Recent deep learning methods rely heavily on datasets, with limited generality to unseen categories. Furthermore, abstracting an object accurately yet with a small number of primitives still remains a challenge. In this paper, we propose a novel non-parametric Bayesian statistical method to infer an abstraction, consisting of an unknown number of geometric primitives, from a point cloud. We model the generation of points as observations sampled from an infinite mixture of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the abstraction as a clustering problem, in which: 1) each point is assigned to a cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation is optimized for each cluster, and 3) a merging post-process is incorporated to provide a concise representation. We conduct extensive experiments on two datasets. The results indicate that our method outperforms the state-of-the-art in terms of accuracy and is generalizable to various types of objects."
Stereo Depth Estimation with Echoes
Authors: Chenghao Zhang; Kun Tian; Bolin Ni; Gaofeng Meng; Bin Fan; Zhaoxiang Zhang; Chunhong Pan
Venue: ECCV 2022 (main)

Abstract: "Stereo depth estimation is particularly amenable to local textured regions while echoes have good depth estimations for global textureless regions, thus the two modalities complement each other. Motivated by the reciprocal relationship between both modalities, in this paper, we propose an end-to-end framework named StereoEchoes for stereo depth estimation with echoes. A Cross-modal Volume Refinement module is designed to transfer the complementary knowledge of the audio modality to the visual modality at feature level. A Relative Depth Uncertainty Estimation module is further proposed to yield pixel-wise confidence for multimodal depth fusion at output space. As there is no dataset for this new problem, we introduce two Stereo-Echo datasets named Stereo-Replica and Stereo-Matterport3D for the first time. Remarkably, we show empirically that our StereoEchoes, on Stereo-Replica and Stereo-Matterport3D, outperforms stereo depth estimation methods by 25%/13.8% RMSE, and surpasses the state-of-the-art audio-visual depth prediction method by 25.3%/42.3% RMSE."
Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Authors: Hanrong Ye; Dan Xu
Venue: ECCV 2022 (main)

Abstract: "Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction. Most existing works encounter a severe limitation of modeling in the locality due to heavy utilization of convolution operations, while learning interactions and inference in a global spatial-position and multi-task context is critical for this problem. In this paper, we propose a novel end-to-end Inverted Pyramid multi-task Transformer (InvPT) to perform simultaneous modeling of spatial positions and multiple tasks in a unified framework. To the best of our knowledge, this is the first work that explores designing a transformer structure for multi-task dense prediction for scene understanding. Besides, it is widely demonstrated that a higher spatial resolution is remarkably beneficial for dense predictions, while it is very challenging for existing transformers to go deeper with higher resolutions due to huge complexity to large spatial size. InvPT presents an efficient UP-Transformer block to learn multi-task feature interaction at gradually increased resolutions, which also incorporates effective self-attention message passing and multi-scale feature aggregation to produce task-specific prediction at a high resolution. Our method achieves superior multi-task performance on NYUD-v2 and PASCAL-Context datasets respectively, and significantly outperforms previous state-of-the-arts. The code is available at https://github.com/prismformore/InvPT."
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
Authors: Yingfei Liu; Tiancai Wang; Xiangyu Zhang; Jian Sun
Venue: ECCV 2022 (main)

Abstract: "In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features. Object query can perceive the 3D position-aware features and perform end-to-end object detection. Till submission, PETR achieves state-of-the-art performance (50.4% NDS and 44.1% mAP) on standard nuScenes dataset and ranks 1st place on the benchmark. It can serve as a simple yet strong baseline for future research."
S2Net: Stochastic Sequential Pointcloud Forecasting
Authors: Xinshuo Weng; Junyu Nan; Kuan-Hui Lee; Rowan McAllister; Adrien Gaidon; Nicholas Rhinehart; Kris M. Kitani
Venue: ECCV 2022 (main)

Abstract: "Predicting futures of surrounding agents is critical for autonomous systems such as self-driving cars. Instead of requiring accurate detection and tracking prior to trajectory prediction, an object agnostic Sequential Pointcloud Forecasting (SPF) task was proposed in prior work, which enables a forecast-then-detect pipeline effective for downstream detection and trajectory prediction. One limitation of prior work is that it forecasts only a deterministic sequence of future point clouds, despite the inherent uncertainty of dynamic scenes. In this work, we tackle the stochastic SPF problem by proposing a generative model with two main components: (1) a conditional variational recurrent neural network that models a temporally-dependent latent space; (2) a pyramid-LSTM that increases the fidelity of predictions with temporally-aligned skip connections. Through experiments on real-world autonomous driving datasets, our stochastic SPF model produces higher-fidelity predictions, reducing Chamfer distances by up to 56.6% compared to its deterministic counterpart. In addition, our model can estimate the uncertainty of predicted points, which can be helpful to downstream tasks."
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
Authors: Mu He; Le Hui; Yikai Bian; Jian Ren; Jin Xie; Jian Yang
Venue: ECCV 2022 (main)

Abstract: "Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performance degradation when directly adopting a model trained on a fixed resolution to evaluate at other different resolutions. In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. Specifically, we propose a simple yet efficient data augmentation method to generate images with arbitrary scales for the same scene. Then, we develop a dual high-resolution network that uses the multi-path encoder and decoder with dense interactions to aggregate multi-scale features for accurate depth inference. Finally, to explicitly learn the scale invariance of the scene depth, we formulate a cross-scale depth consistency loss on depth predictions with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2 datasets demonstrate that RA-Depth not only achieves state-of-the-art performance, but also exhibits a good ability of resolution adaptation."
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation
Authors: Haobo Yuan; Xiangtai Li; Yibo Yang; Guangliang Cheng; Jing Zhang; Yunhai Tong; Lefei Zhang; Dacheng Tao
Venue: ECCV 2022 (main)

Abstract: "The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously. The previous work solves this task by extending the existing panoptic segmentation method with an extra dense depth prediction and instance tracking head. However, the relationship between the depth and panoptic segmentation is not well explored -- simply combining existing methods leads to competition and needs carefully weight balancing. In this paper, we present PolyphonicFormer, a vision transformer to unify these sub-tasks under the DVPS task and lead to more robust results. Our principal insight is that the depth can be harmonized with the panoptic segmentation with our proposed new paradigm of predicting instance level depth maps with object queries. Then the relationship between the two tasks via query-based learning is explored. From the experiments, we demonstrate the benefits of our design from both depth estimation and panoptic segmentation aspects. Since each thing query also encodes the instance-wise information, it is natural to perform tracking directly with appearance learning. Our method achieves state-of-the-art results on two DVPS datasets (Semantic KITTI, Cityscapes), and ranks 1st on the ICCV-2021 BMTT Challenge video + depth track. Code is available at https://github.com/HarborYuan/PolyphonicFormer."
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
Authors: Qingyong Hu; Bo Yang; Guangchi Fang; Yulan Guo; Aleš Leonardis; Niki Trigoni; Andrew Markham
Venue: ECCV 2022 (main)

Abstract: "Labelling point clouds fully is highly time-consuming and costly. As larger point cloud datasets containing billions of points become more common, we ask whether the full annotation is even necessary, demonstrating that existing baselines designed under a fully annotated assumption only degrade slightly even when faced with 1% random point annotations. However, beyond this point, e.g. at 0.1% annotations, segmentation accuracy is unacceptably low. We observe that, as point clouds are samples of the 3D world, the distribution of points in a local neighbourhood is relatively homogeneous, exhibiting strong semantic similarity. Motivated by this, we propose a new weak supervision method to implicitly augment these highly sparse supervision signals. Extensive experiments demonstrate that the proposed Semantic Query Network (SQN) achieves state-of-the-art performance on seven large-scale open datasets under weak supervision schemes, while requiring only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort."
PointMixer: MLP-Mixer for Point Cloud Understanding
Authors: Jaesung Choe; Chunghyun Park; Francois Rameau; Jaesik Park; In So Kweon
Venue: ECCV 2022 (main)

Abstract: MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and Transformer. Despite its simplicity compared to Transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in image recognition tasks. Unlike images, point clouds are inherently sparse, unordered and irregular, which limits the direct use of MLP-Mixer for point cloud understanding. To overcome these limitations, we propose PointMixer, a universal point set operator that facilitates information sharing among unstructured 3D point cloud. By simply replacing token-mixing MLPs with Softmax function, PointMixer can “mix" features within/between point sets. By doing so, PointMixer can be broadly used for intra-set, inter-set, and hierarchical-set mixing. We demonstrate that various channel-wise feature aggregation in numerous point sets is better than self-attention layers or dense token-wise interaction in a view of parameter efficiency and accuracy. Extensive experiments show the competitive or superior performance of PointMixer in semantic segmentation, classification, and reconstruction against Transformer-based methods.
Initialization and Alignment for Adversarial Texture Optimization
Authors: Xiaoming Zhao; Zhizhen Zhao; Alexander G. Schwing
Venue: ECCV 2022 (main)

Abstract: "While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent methods, e.g., adversarial texture optimization, better handle lower-quality data obtained from hand-held devices, we find them to still struggle frequently. To improve robustness, particularly of recent adversarial texture optimization, we develop an explicit initialization and an alignment procedure. It deals with complex geometry due to a robust mapping of the geometry to the texture map and a hard-assignment-based initialization. It deals with misalignment of geometry and images by integrating fast image-alignment into the texture refinement optimization. We demonstrate efficacy of our texture generation on a dataset of 11 scenes with a total of 2807 frames, observing 7.8% and 11.1% relative improvements regarding perceptual and sharpness measurements."
MOTR: End-to-End Multiple-Object Tracking with TRansformer
Authors: Fangao Zeng; Bin Dong; Yuang Zhang; Tiancai Wang; Xiangyu Zhang; Yichen Wei
Venue: ECCV 2022 (main)

Abstract: "Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing methods track by associating detections through motion-based and appearance-based similarity heuristics. The post-processing nature of association prevents end-to-end exploitation of temporal variations in video sequence. In this paper, we propose MOTR, which extends DETR \cite{carion2020detr} and introduces “track query” to model the tracked instances in the entire video. Track query is transferred and updated frame-by-frame to perform iterative prediction over time. We propose tracklet-aware label assignment to train track queries and newborn object queries. We further propose temporal aggregation network and collective average loss to enhance temporal relation modeling. Experimental results on DanceTrack show that MOTR significantly outperforms state-of-the-art method, ByteTrack by 6.5\% on HOTA metric. On MOT17, MOTR outperforms our concurrent works, TrackFormer and TransTrack, on association performance. MOTR can serve as a stronger baseline for future research on temporal modeling and Transformer-based trackers. Code is available at \url{https://github.com/anonymous4669/MOTR}."
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing
Authors: Sijie Zhu; Zhe Lin; Scott Cohen; Jason Kuen; Zhifei Zhang; Chen Chen
Venue: ECCV 2022 (main)

Abstract: "Compositing-aware object search aims to find the most compatible objects for compositing given a background image and a query bounding box. Previous works focus on learning compatibility between the foreground object and background, but fail to learn other important factors from large-scale data, i.e. geometry and lighting. To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing. Remarkably, it achieves state-of-the-art results on the CAIS dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay and Open Images. In addition, our method can effectively handle non-box scenarios, where users only provide background images without any input bounding box. Experiments are conducted on real-world images to showcase applications of the proposed method for compositing-aware search and automatic location/scale prediction for the foreground object."