Reviewerly/exHarmony huggingface dataset:
Real-Time Trajectory Planning for Aerial Perching

This paper presents a novel trajectory planning method for aerial perching. Compared with the existing work, the terminal states and the trajectory durations can be adjusted adaptively, instead of being determined in advance. Further-more, our planner is able to minimize the tangential relative speed on the premise of safety and dynamic feasibility. This feature is especially notable on micro aerial robots with low maneuverability or scenarios where the space is not enough. Moreover, we design a flexible transformation strategy to eliminate terminal constraints along with reducing optimization variables. Besides, we take precise SE(3) motion planning into account to ensure that the drone would not touch the landing platform until the last moment. The proposed method is validated onboard by a palm-sized micro aerial robot with quite limited thrust and moment (thrust-to-weight ratio 1.7) perching on a mobile inclined surface. Sufficient experimental results show that our planner generates an optimal trajectory within 20ms, and replans with warm start in 2ms.
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness.
TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting

Counting repetitive actions are widely seen in human activities such as physical exercise. Existing methods focus on performing repetitive action counting in short videos, which is tough for dealing with longer videos in more realistic scenarios. In the data-driven era, the degradation of such generalization capability is mainly attributed to the lack of long video datasets. To complement this margin, we introduce a new large-scale repetitive action counting dataset covering a wide variety of video lengths, along with more realistic situations where action interruption or action inconsistencies occur in the video. Besides, we also provide a fine-grained annotation of the action cycles instead of just counting annotation along with a numerical value. Such a dataset contains 1,451 videos with about 20,000 annotations, which is more challenging. For repetitive action counting towards more realistic scenarios, we further propose encoding multi-scale temporal correlation with transformers that can take into account both performance and efficiency. Furthermore, with the help of fine-grained annotation of action cycles, we propose a density map regression-based method to predict the action period, which yields better performance with sufficient interpretability. Our proposed method outperforms state-of-the-art methods on all datasets and also achieves better performance on the unseen dataset without fine-tuning. The dataset and code are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://svip-lab.github.io/dataset/RepCount_dataset.html.
Efficient Classification of Very Large Images with Tiny Objects

An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with tiny informative objects. Specifically, these classification tasks face two key challenges: i) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images; and ii) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (ROI) to image ratio. However, most of the current convolutional neural networks (CNNs) are designed for image classification datasets that have relatively large ROIs and small image sizes (sub-megapixel). Existing approaches have addressed these two challenges in isolation. We present an end-to-end CNN model termed Zoom-In network that leverages hierarchical attention sampling for classification of large images with tiny objects using a single GPU. We evaluate our method on four large-image histopathology, road-scene and satellite imaging datasets, and one gigapixel pathology dataset. Experimental results show that our model achieves higher accuracy than existing methods while requiring less memory resources.
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds

Weakly-supervised semantic segmentation (WSSS) has recently gained much attention for its promise to train segmentation models only with image-level labels. Existing WSSS methods commonly argue that the sparse coverage of CAM incurs the performance bottleneck of WSSS. This paper provides analytical and empirical evidence that the actual bottleneck may not be sparse coverage but a global thresholding scheme applied after CAM. Then, we show that this issue can be mitigated by satisfying two conditions; 1) reducing the imbalance in the foreground activation and 2) increasing the gap between the foreground and the background activation. Based on these findings, we propose a novel activation manipulation network with a per-pixel classification loss and a label conditioning module. Per-pixel classification naturally induces two-level activation in activation maps, which can penalize the most discriminative parts, promote the less discriminative parts, and deactivate the background regions. Label conditioning imposes that the output label of pseudo-masks should be any of true image-level labels; it penalizes the wrong activation assigned to non-target classes. Based on extensive analysis and evaluations, we demonstrate that each component helps produce accurate pseudo-masks, achieving the robustness against the choice of the global threshold. Finally, our model achieves state-of-the-art records on both PAS-CAL VOC 2012 and MS COCO 2014 datasets. The code is available at https://github.com/gaviotas/AMN.
Motion-modulated Temporal Fragment Alignment Network For Few-Shot Action Recognition

While the majority of FSL models focus on image classification, the extension to action recognition is rather challenging due to the additional temporal dimension in videos. To address this issue, we propose an end-to-end Motion-modulated Temporal Fragment Alignment Network (MT-FAN) by jointly exploring the task-specific motion modulation and the multi-level temporal fragment alignment for Few-Shot Action Recognition (FSAR). The proposed MT-FAN model enjoys several merits. First, we design a motion modulator conditioned on the learned task-specific motion embeddings, which can activate the channels related to the task-shared motion patterns for each frame. Second, a segment attention mechanism is proposed to automatically discover the higher-level segments for multi-level temporal fragment alignment, which encompasses the frame-to-frame, segment-to-segment, and segment-to-frame alignments. To the best of our knowledge, this is the first work to exploit task-specific motion modulation for FSAR. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against the state-of-the-art FSAR methods.
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis

We propose a new method for reconstructing control-lable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene repre-sentation on the mesh surface points and signed distances from the surface of a human body mesh. We identify an indistinguishability issue that arises when a point in 3D space is mapped to its nearest surface point on a mesh for learning surface-aligned neural scene representation. To address this issue, we propose projecting a point onto a mesh surface using a barycentric interpolation with modi-fied vertex normals. Experiments with the ZJU-MoCap and Human3.6M datasets show that our approach achieves a higher quality in a novel-view and novel-pose synthesis than existing methods. We also demonstrate that our method eas-ily supports the control of body shape and clothes. Project page: https://pfnet-research.github.io/surface-aligned-nerf/
M2TS

Source code summarization aims to generate natural language descriptions of code snippets. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code. Traditional models traverse ASTs as sequences or split ASTs into paths as input. However, the former loses the structural properties of ASTs, and the latter destroys the overall structure of ASTs. Therefore, comprehensively capturing the structural features of ASTs in learning code representations for source code summarization remains a challenging problem to be solved. In this paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer for source code Summarization. M2TS uses a multi-scale AST feature extraction method, which can extract the structures of ASTs more completely and accurately at multiple local and global levels. To complement missing semantic information in ASTs, we also obtain code token features, and further combine them with the extracted AST features using a cross modality fusion method that not only fuses the syntactic and contextual semantic information of source code, but also highlights the key features of each modality. We conduct experiments on two Java and one Python datasets, and the experimental results demonstrate that M2TS outperforms current state-of-the-art methods. We release our code at https://github.com/TranSMS/M2TS.
Active Teacher for Semi-Supervised Object Detection

In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source code are available at: https://github.com/HunterJLin/ActiveTeacher for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, i.e., Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, i.e. 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications.
Mixed-Integer and Conditional Trajectory Planning for an Autonomous Mining Truck in Loading/Dumping Scenarios: A Global Optimization Approach

Trajectory planning for a heavy-duty mining truck near the loading/dumping sites of an open-pit mine is difficult. As opposed to trajectory planning for a small-sized passenger car in a parking lot, trajectory planning for a heavy-duty mining truck involves complex factors in vehicle kinematics and environment. These factors make the concerned trajectory planning scheme a mixed-integer nonlinear program (MINLP) incorporated with conditional constraints (denoted as C-MINLP). MINLP solvers can neither deal with conditional constraints nor find global optima in real time. Instead of solving the C-MINLP directly, we build a from-coarse-to-fine framework so that the coupled difficulties (the mixed integral variables, conditional constraints, and the demand for global optimality) are divided and conquered. At the coarse search stage, a global-optimality-enhanced hybrid A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> search algorithm is proposed to find a near-optimal coarse trajectory with the mixed integral variables, conditional kinematic constraints, and global optimality considered. The coarse trajectory is further polished at the refinement stage, wherein the nominal C-MINLP is simplified as a small-scale NLP. The solution to the NLP is an optimized trajectory, which does not violate the complex constraints in the nominal C-MINLP. This indicates that conversion from the C-MINLP to an NLP is efficient with the help of a high-quality coarse trajectory.
BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information

Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to directly regress the SMAL animal model, with additional limb scale parameters, from images. Our method, called BARC (Breed-Augmented Regression using Classification), goes beyond prior work in several important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. But, even with a better shape model, the problem of regressing dog shape from an image is still challenging because we lack paired images with 3D ground truth. To compensate for the lack of paired data, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss consisting of two parts: One term encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one, a breed classification loss, helps to produce recognizable breed-specific shapes. Through ablation studies, we find that our breed losses significantly improve shape accuracy over a baseline without them. We also compare BARC qualitatively to WLDO with a perceptual study and find that our approach produces dogs that are significantly more realistic. This work shows that a-priori information about genetic similarity can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/.
Partial Multilabel Learning Using Fuzzy Neighborhood-Based Ball Clustering and Kernel Extreme Learning Machine

Partial multilabel learning (PML) has attracted considerable interest from scholars. Most PML models construct objective functions and optimize target parameters, which add noise to the training process and results in a poor classification effect. In addition, feature correlation is limited to linear transformations while ignoring the complex relationships among features. In this study, we develop a novel PML model with fuzzy neighborhood-based ball clustering and kernel extreme learning machine (KELM). To reduce the interference from noise, ball <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering is introduced to preprocess partial multilabel data and initialize ball clustering. A new ball clustering model based on fuzzy neighborhood is first designed to address partial multilabel systems. The particle-ball fusion strategy is developed to merge the particle balls reasonably, and the fuzzy membership function and label enhancement are studied for the subsequent training process. Then novel KELM with feature transformation matrix of training data is produced to analyze the nonlinear relationships among features. Finally, a nonsmooth convex objective function with the regression model is constructed to analyze the complex nonlinear relationships among features, and the optimal solutions of three objective parameters are solved by accelerated proximal gradient optimization. Experiments on 14 datasets reveal the effectiveness of the developed algorithm.
Towards Accurate Facial Landmark Detection via Cascaded Transformers

Accurate facial landmarks are essential prerequisites for many tasks related to human faces. In this paper, an accurate facial landmark detector is proposed based on cascaded transformers. We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end. With self-attention in transformers, our model can inherently exploit the structured relationships between landmarks, which would benefit landmark detection under challenging conditions such as large pose and occlusion. During cascaded refinement, our model is able to extract the most relevant image features around the target landmark for coordinate prediction, based on deformable attention mechanism, thus bringing more accurate alignment. In addition, we propose a novel decoder that refines image features and landmark positions simultaneously. With few parameter increasing, the detection performance improves further. Our model achieves new state-of-the-art performance on several standard facial landmark detection benchmarks, and shows good generalization ability in cross-dataset evaluation.
Physically Disentangled Intra- and Inter-domain Adaptation for Varicolored Haze Removal

Learning-based image dehazing methods have achieved marvelous progress during the past few years. On one hand, most approaches heavily rely on synthetic data and may face difficulties to generalize well in real scenes, due to the huge domain gap between synthetic and real images. On the other hand, very few works have considered the varicolored haze, caused by chromatic casts in real scenes. In this work, our goal is to handle the new task: real-world varicolored haze removal. To this end, we propose a physically disentangled joint intra- and inter-domain adaptation paradigm, in which intra-domain adaptation focuses on color correction and inter-domain procedure transfers knowledge between synthetic and real domains. We first learn to physically disentangle haze images into three components complying with the scattering model: background, transmission map, and atmospheric light. Since haze color is determined by atmospheric light, we perform intra-domain adaptation by specifically translating atmospheric light from varicolored space to unified color-balanced space, and then reconstructing color-balanced haze image through the scattering model. Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components. Then we can reconstruct both identity and domain-translated haze images with self-consistency and adversarial loss. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art for real varicolored image dehazing.
AUV Dynamic Obstacle Avoidance Method Based on Improved PPO Algorithm

A reasonable obstacle avoidance method for AUV 3D path planning is difficult. Existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, this study designs an obstacle collision prediction model (CPM). Based on the results of the simulation operation of the obstacle inertial motion, the safety of the AUV navigation is evaluated to improve the sensitivity of the model to dynamic obstacles. Then, the learning ability of the sequence sample data is enhanced by combining it with the long short-term memory (LSTM) network, and the training efficiency and effect of the algorithm are improved. The trained proximal policy optimization (PPO) network can output reasonable actions to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments, and successfully realizes dynamic obstacle avoidance of AUV in different three-dimensional unknown environments, providing theoretical and technical support for real path planning.
Toward Practical Monocular Indoor Depth Estimation

The majority of prior monocular depth estimation meth-ods without groundtruth depth guidance focus on driving scenarios. We show that such methods generalize poorly to unseen complex indoor scenes, where objects are cluttered and arbitrarily arranged in the near field. To obtain more robustness, we propose a structure distillation approach to learn knacks from an off-the-shelf relative depth estima-tor that produces structured but metric-agnostic depth. By combining structure distillation with a branch that learns metrics from left-right consistency, we attain structured and metric depth for generic indoor scenes and make inferences in real-time. To facilitate learning and evaluation, we col-lect SimSIN, a dataset from simulation with thousands of environments, and UniSIN, a dataset that contains about 500 real scan sequences of generic indoor environments. We experiment in both sim-to-real and real-to-real settings, and show improvements, as well as in downstream applications using our depth maps. This work provides a full study, covering methods, data, and applications aspects.