GenAI4ELab/papercli-papers huggingface dataset:
You Should Look at All Objects
Authors: Zhenchao Jin; Dongdong Yu; Luchuan Song; Zehuan Yuan; Lequan Yu
Venue: ECCV 2022 (main)

Abstract: "Feature pyramid network (FPN) is one of the key components for object detectors. However, there is a long-standing puzzle for researchers that the detection performance of large-scale objects are usually suppressed after introducing FPN. To this end, this paper first revisits FPN in the detection framework and reveals the nature of the success of FPN from the perspective of optimization. Then, we point out that the degraded performance of large-scale objects is due to the arising of improper back-propagation paths after integrating FPN. It makes each level of the backbone network only has the ability to look at the objects within a certain scale range. Based on these analysis, two feasible strategies are proposed to enable each level of the backbone to look at all objects in the FPN-based detection frameworks. Specifically, one is to introduce auxiliary objective functions to make each backbone level directly receive the back-propagation signals of various-scale objects during training. The other is to construct the feature pyramid in a more reasonable way to avoid the irrational back-propagation paths. Extensive experiments on the COCO benchmark validate the soundness of our analysis and the effectiveness of our methods. Without bells and whistles, we demonstrate that our method achieves solid improvements (more than 2%) on various detection frameworks: one-stage, two-stage, anchor-based, anchor-free and transformer-based detectors."
Detecting Twenty-Thousand Classes Using Image-Level Supervision
Authors: Xingyi Zhou; Rohit Girdhar; Armand Joulin; Philipp Krähenbühl; Ishan Misra
Venue: ECCV 2022 (main)

Abstract: "Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets are larger and easier to collect. We propose Detic, which simply trains the classifiers of a detector on image classification data and thus expands the vocabulary of detectors to tens of thousands of concepts. Unlike prior work, Detic does not need complex assignment schemes to assign image labels to boxes based on model predictions, making it much easier to implement and compatible with a range of detection architectures and backbones. Our results show that Detic yields excellent detectors even for classes without box annotations. It outperforms prior work on both open-vocabulary and long-tail detection benchmarks. Detic provides a gain of 2.4 mAP for all classes and 8.3 mAP for novel classes on the open-vocabulary LVIS benchmark. On the standard LVIS benchmark, Detic obtains 41.7 mAP when evaluated on all classes, or only rare classes, hence closing the gap in performance for object categories with few samples. For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without finetuning. Code is provided in the supplementary."
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
Authors: Hongyang Li; Jiehong Lin; Kui Jia
Venue: ECCV 2022 (main)

Abstract: "Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step away from the true ones of object pose estimation, making the learning suboptimal for the end task. In this paper, we address this shortcoming by introducing a new method of Deep Correspondence Learning Network for direct 6D object pose estimation, shortened as DCL-Net. Specifically, DCL-Net employs dual newly proposed Feature Disengagement and Alignment (FDA) modules to establish, in the feature space, partial-to-partial correspondence and complete-to-complete one for partial object observation and its complete CAD model, respectively, which result in aggregated pose and match feature pairs from two coordinate systems; these two FDA modules thus bring complementary advantages. The match feature pairs are used to learn confidence scores for measuring the qualities of deep correspondence, while the pose ones are weighted by confidence scores for direct object pose regression. A confidence-based pose refinement network is also proposed to further improve pose precision in an iterative manner. Extensive experiments show that DCL-Net outperforms existing methods on three benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD; ablation studies also confirm the efficacy of our novel designs. Our code is released publicly at https://github.com/Gorilla-Lab-SCUT/DCL-Net."
Monocular 3D Object Detection with Depth from Motion
Authors: Tai Wang; Jiangmiao Pang; Dahua Lin
Venue: ECCV 2022 (main)

Abstract: "Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings. It is notably difficult as a single image can not provide any clues for predicting absolute depth values. Motivated by binocular methods for 3D object detection, we take advantage of the strong geometry structure provided by camera ego-motion for accurate object depth estimation and detection. We first make a theoretical analysis on this general two-view case and notice two challenges: 1) Cumulative errors from multiple estimations that make the direct prediction intractable; 2) Inherent dilemmas caused by static cameras and matching ambiguity. Accordingly, we establish the stereo correspondence with a geometry-aware cost volume as the alternative for depth estimation and further compensate it with monocular understanding to address the second problem. Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon. We also present a pose-free DfM to make it usable when the camera pose is unavailable. Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark. Detailed quantitative and qualitative analyses also validate our theoretical conclusions. The code is released at https://github.com/Tai-Wang/Depth-from-Motion."
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
Authors: Yilin Wen; Xiangyu Li; Hao Pan; Lei Yang; Zheng Wang; Taku Komura; Wenping Wang
Venue: ECCV 2022 (main)

Abstract: "Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects. Building on a well-known auto-encoding framework to cope with object symmetry and the lack of labeled training data, we achieve scalability by disentangling the latent representation of auto-encoder into shape and pose sub-spaces. The latent shape space models the similarity of different objects through contrastive metric learning, and the latent pose code is compared with canonical rotations for rotation retrieval. Because different object symmetries induce inconsistent latent pose spaces, we re-entangle the shape representation with canonical rotations to generate shape-dependent pose codebooks for rotation retrieval. We show state-of-the-art performance on two benchmarks containing textureless CAD objects without category and daily objects with categories respectively, and further demonstrate improved scalability by extending to a more challenging setting of daily objects across categories."
Distilling Object Detectors with Global Knowledge
Authors: Sanli Tang; Zhongyu Zhang; Zhanzhan Cheng; Jing Lu; Yunlu Xu; Yi Niu; Fan He
Venue: ECCV 2022 (main)

Abstract: "Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations, which is the instance-level knowledge only from the teacher model, i.e., the local knowledge. However, the empirical studies show that the local knowledge is much noisy in object detection tasks, especially on the blurred, occluded, or small instances. Thus, a more intrinsic approach is to measure the representations of instances w.r.t. a group of common basis vectors in the two feature spaces of the teacher and the student detectors, i.e., global knowledge. Then, the distilling algorithm can be applied as space alignment. To this end, a novel prototype generation module (PGM) is proposed to find the common basis vectors, dubbed prototypes, in the two feature spaces. Then, a robust distilling module (RDM) is applied to construct the global knowledge based on the prototypes and filtrate noisy global and local knowledge by measuring the discrepancy of the representations in two feature spaces. Experiments with Faster-RCNN and RetinaNet on PASCAL and COCO datasets show that our method achieves the best performance for distilling object detectors with various backbones, which even surpasses the performance of the teacher model. We also show that the existing methods can be easily combined with global knowledge and obtain further improvement. Code is available: https://github.com/hikvision-research/DAVAR-Lab-ML."
Unifying Visual Perception by Dispersible Points Learning
Authors: Jianming Liang; Guanglu Song; Biao Leng; Yu Liu
Venue: ECCV 2022 (main)

Abstract: "We present a conceptually simple, flexible, and universal visual perception head for variant visual tasks, e.g., classification, object detection, instance segmentation and pose estimation, and different frameworks, such as one-stage or two-stage pipelines. Our approach effectively identifies an object in an image while simultaneously generating a high-quality bounding box or contour-based segmentation mask or set of keypoints. The method, called UniHead, views different visual perception tasks as the dispersible points learning via the transformer encoder architecture. Given a fixed spatial coordinate, UniHead adaptively scatters it to different spatial points and reasons about their relations by transformer encoder. It directly outputs the final set of predictions in the form of multiple points, allowing us to perform different visual tasks in different frameworks with the same head design. We show extensive evaluations on ImageNet classification and all three tracks of the COCO suite of challenges, including object detection, instance segmentation and pose estimation. Without bells and whistles, UniHead can unify these visual tasks via a single visual head design and achieve comparable performance compared to expert models developed for each task. We hope our simple and universal UniHead will serve as a solid baseline and help promote universal visual perception research. Code and models are available at https://github.com/Sense-X/UniHead."
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Authors: Gang Li; Xiang Li; Yujie Wang; Yichao Wu; Ding Liang; Shanshan Zhang
Venue: ECCV 2022 (main)

Abstract: "In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We observe that these two techniques currently neglect some important properties of object detection, hindering efficient learning on unlabeled data. Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance. To address the problems incurred by noisy pseudo boxes, we design Noisy Pseudo box Learning (NPL) that includes Prediction-guided Label Assignment (PLA) and Positive-proposal Consistency Voting (PCV). PLA relies on model predictions to assign labels and makes it robust to even coarse pseudo boxes; while PCV leverages the regression consistency of positive proposals to reflect the localization quality of pseudo boxes. Furthermore, in consistency training, we propose Multi-view Scale-invariant Learning (MSL) that includes mechanisms of both label- and feature-level consistency, where feature consistency is achieved by aligning shifted feature pyramids between two images with identical content but varied scales. On COCO benchmark, our method, termed PSEudo labeling and COnsistency training (PseCo), outperforms the SOTA (Soft Teacher) by 2.0, 1.8, 2.0 points under 1%, 5%, and 10% labelling ratios, respectively. It also significantly improves the learning efficiency for SSOD, e.g., PseCo halves the training time of the SOTA approach but achieves even better performance. Code is available at https://github.com/ligang-cs/PseCo."
Exploring Resolution and Degradation Clues As Self-Supervised Signal for Low Quality Object Detection
Authors: Ziteng Cui; Yingying Zhu; Lin Gu; Guo-Jun Qi; Xiaoxiao Li; Renrui Zhang; Zenghui Zhang; Tatsuya Harada
Venue: ECCV 2022 (main)

Abstract: "Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low qual-ity images. Most of these algorithms assume the degradation is fixed andknown a priori. However, in pratical, either the real degrdation or optimalup-sampling ratio rate is unknown or differs from assumption, leading toa deteriorating performance for both the pre-processing module and theconsequent high-level task such as object detection. Here, we propose anovel self-supervised framework to detect objects in degraded low res-olution images. We utilizes the downsampling degradation as a kind oftransformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions.The Auto Encoding Resolution in Self-supervision (AERIS) frameworkcould further take the advantage of advanced SR architectures with anarbitrary resolution restoring decoder to reconstruct the original corre-spondence from the degraded input image. Both the representation learn-ing and object detection are optimized jointly in an end-to-end trainingfashion. The generic AERIS frameworkcould be implemented on variousmainstream object detection architectures from CNN to Transformer.The extensive experiments show that our methods has achieved supe-rior performance compared with existing methods when facing variantdegradation situations.We will release the open source code."
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
Authors: Wufei Ma; Angtian Wang; Alan Yuille; Adam Kortylewski
Venue: ECCV 2022 (main)

Abstract: "We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of the neural feature activations at each mesh vertex to perform pose estimation through differentiable rendering. A common problem of rendering-based approaches is that they rely on bounding box proposals, which do not convey information about the 3D rotation of the object and are not reliable when objects are partially occluded. Instead, we introduce a coarse-to-fine optimization strategy that utilizes the rendering process to estimate a sparse set of 6D object proposals, which are subsequently refined with gradient-based optimization. The key to enabling the convergence of our approach is a neural feature representation that is trained to be scale- and rotation-invariant using contrastive learning. Our experiments demonstrate an enhanced category-level 6D pose estimation performance compared to prior work, particularly under strong partial occlusion."
"Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection"
Authors: Maoxun Yuan; Yinyan Wang; Xingxing Wei
Venue: ECCV 2022 (main)

Abstract: "Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, the paired images are used in many fields, such as multispectral pedestrian detection, RGB-IR crowd counting and RGB-IR salient object detection. Compared with natural RGB-IR images, we find detection in aerial RGB-IR images suffers from cross-modal weakly misalignment problems, which are manifested in the position, size and angle deviations of the same object. In this paper, we mainly address the challenge of cross-modal weakly misalignment in aerial RGB-IR images. Specifically, we firstly explain and analyze the cause of the weakly misalignment problem. Then, we propose a Translation-Scale-Rotation Alignment (TSRA) module to address the problem by calibrating the feature maps from these two modalities. The module predicts the deviation between two modality objects through an alignment process and utilizes Modality-Selection (MS) strategy to improve the performance of alignment. Finally, a two-stream feature alignment detector (TSFADet) based on the TSRA module is constructed for RGB-IR object detection in aerial images. With comprehensive experiments on the public DroneVehicle datasets, we verify that our method reduces the effect of the cross-modal misalignment and achieve robust detection results."
RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection
Authors: Chang Xu; Jinwang Wang; Wen Yang; Huai Yu; Lei Yu; Gui-Song Xia
Venue: ECCV 2022 (main)

Abstract: "Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on tiny object detection tasks. In this paper, we point out that either box prior in the anchor-based detector or point prior in the anchor-free detector is sub-optimal for tiny objects. Our key observation is that the current anchor-based or anchor-free label assignment paradigms will incur many outlier tiny-sized ground truth samples, leading to detectors imposing less focus on the tiny objects. To this end, we propose a Gaussian Receptive Field based Label Assignment (RFLA) strategy for tiny object detection. Specifically, RFLA first utilizes the prior information that the feature receptive field follows Gaussian distribution. Then, instead of assigning samples with IoU or center sampling strategy, a new Receptive Field Distance (RFD) is proposed to directly measure the similarity between the Gaussian receptive field and ground truth. Considering that the IoU-threshold based and center sampling strategy are skewed to large objects, we further design a Hierarchical Label Assignment (HLA) module based on RFD to achieve balanced learning for tiny objects. Extensive experiments on four datasets demonstrate the effectiveness of the proposed methods. Especially, our approach outperforms the state-of-the-art competitors with 4.0 AP points on the AI-TOD dataset. Codes are available at https://github.com/Chasel-Tsui/mmdet-rfla."
Rethinking IoU-Based Optimization for Single-Stage 3D Object Detection
Authors: Hualian Sheng; Sijia Cai; Na Zhao; Bing Deng; Jianqiang Huang; Xian-Sheng Hua; Min-Jian Zhao; Gim Hee Lee
Venue: ECCV 2022 (main)

Abstract: "Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors. Recently, several 3D object detection methods adopt IoU-based optimization and directly replace the 2D IoU with 3D IoU. However, such a direct computation in 3D is very costly due to the complex implementation and inefficient backward operations. Moreover, 3D IoU-based optimization is sub-optimal as it is sensitive to rotation and thus can cause training instability and detection performance deterioration. In this paper, we propose a novel Rotation-Decoupled IoU (RDIoU) method that can mitigate the rotation-sensitivity issue, and produce more efficient optimization objectives compared with 3D IoU during the training stage. Specifically, our RDIoU simplifies the complex interactions of regression parameters by decoupling the rotation variable as an independent term, yet preserving the geometry of 3D IoU. By incorporating RDIoU into both the regression and classification branches, the network is encouraged to learn more precise bounding boxes and concurrently overcome the misalignment issue between classification and regression. Extensive experiments on the benchmark KITTI and Waymo Open Dataset validate that our RDIoU method can bring substantial improvement for the single-stage 3D object detection. Our code will be available upon paper acceptance."
TD-Road: Top-Down Road Network Extraction with Holistic Graph Construction
Authors: Yang He; Ravi Garg; Amber Roy Chowdhury
Venue: ECCV 2022 (main)

Abstract: "Graph-based approaches have been becoming increasingly popular in road network extraction, in addition to segmentation-based methods. Road networks are represented as graph structures, being able to explicitly define the topology structures and avoid the ambiguity of segmentation masks, such as between a real junction area and multiple separate roads in different heights. In contrast to the bottom-up graph-based approaches, which rely on orientation information, we propose a novel top-down approach to generate road network graphs with a holistic model, namely TD-Road. We decompose road extraction as two subtasks: key point prediction and connectedness prediction. We directly apply graph structures (i.e., locations of node and connections between them) as training supervisions for neural networks and generate road graph outputs in inference, instead of learning some intermediate properties of a graph structure (e.g., orientations or distances for the next move). Our network integrates a relation inference module with key point prediction, to capture connections between neighboring points and outputs the final road graphs with no post-processing steps required. Extensive experiments are conducted on challenging datasets, including City-Scale and SpaceNet to show the effectiveness and simplicity of our method, that the proposed method achieves remarkable results compared with previous state-of-the-art methods."
Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection
Authors: Shuang Wu; Wenjie Pei; Dianwen Mei; Fanglin Chen; Jiandong Tian; Guangming Lu
Venue: ECCV 2022 (main)

Abstract: "Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true since the object detector can hardly distinguish between class-agnostic knowledge and class-specific knowledge automatically without explicit modeling. In this work we propose to learn three types of class-agnostic commonalities between base and novel classes explicitly: recognition-related semantic commonalities, localization-related semantic commonalities and distribution commonalities. We design a unified distillation framework based on a memory bank, which is able to perform distillation of all three types of commonalities jointly and efficiently. Extensive experiments demonstrate that our method can be readily integrated into most of existing fine-tuning based methods and consistently improve the performance by a large margin."