Object Recognition Research Articles

Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.

Primates can recognize objects despite 3D geometric variations such as in-depth rotations. The computational mechanisms that give rise to such invariances are yet to be fully understood. A curious case of partial invariance occurs in the macaque face-patch AL and in fully connected layers of deep convolutional networks in which neurons respond similarly to mirror-symmetric view (e.g., left and right profiles). Why does this tuning develop? Here, we propose a simple learning-driven explanation for mirror-symmetric viewpoint tuning. We show that mirror-symmetric viewpoint tuning for faces emerges in the fully connected layers of convolutional deep neural networks trained on object recognition tasks, even when the training dataset does not include faces. First, using 3D objects rendered from multiple views as test stimuli, we demonstrate that mirror-symmetric viewpoint tuning in convolutional neural network models is not unique to faces: it emerges for multiple object categories with bilateral symmetry. Second, we show why this invariance emerges in the models. Learning to discriminate among bilaterally symmetric object categories induces reflection-equivariant intermediate representations. AL-like mirror-symmetric tuning is achieved when such equivariant responses are spatially pooled by downstream units with sufficiently large receptive fields. These results explain how mirror-symmetric viewpoint tuning can emerge in neural networks, providing a theory of how they might emerge in the primate brain. Our theory predicts that mirror-symmetric viewpoint tuning can emerge as a consequence of exposure to bilaterally symmetric objects beyond the category of faces, and that it can generalize beyond previously experienced object categories.

Peripherally-derived LGI1-reactive monoclonal antibodies cause epileptic seizures in vivo.

One striking clinical hallmark in patients with autoantibodies to leucine-rich glioma inactivated 1 (LGI1) is the very frequent focal seizure semiologies, including faciobrachial dystonic seizures (FBDS), in addition to the amnesia. Polyclonal serum IgGs have successfully modelled the cognitive changes in vivo but not seizures. Hence, it remains unclear whether LGI1-autoantibodies are sufficient to cause seizures. We tested this with the molecularly precise monoclonal antibodies directed against LGI1 (LGI1-mAbs), derived from patient circulating B cells. These were directed towards both major domains of LGI1, LRR and EPTP and infused intracerebroventricularly over 7 days into juvenile male Wistar rats using osmotic pumps. Continuous wireless EEG was recorded from a depth electrode placed in hippocampal CA3 plus behavioural tests for memory and hyperexcitability were performed. Following infusion completion (Day 9), post-mortem brain slices were studied for antibody binding and effects on Kv1.1. The LGI1-mAbs bound most strongly in the hippocampal CA3 region and induced a significant reduction in Kv1.1 cluster number in this subfield. By comparison to control-Ab injected rats video-EEG analysis over 9 days revealed convulsive and non-convulsive seizure activity in rats infused with LGI1-mAbs, with a significant number of ictal events. Memory was not impaired in the novel object recognition test. Peripherally-derived human LGI1-mAbs infused into rodent CSF provide strong evidence of direct in vivo epileptogenesis with molecular correlations. These findings fulfill criteria for LGI1-antibodies in seizure causation.

Analysis of Music Brand Similarity Levels Using a Visual Computing Approach

Currently, many trademarks have emerged, especially for music equipment products circulating in the community. Especially in Indonesia, musical instrument brands with various models have emerged. The problem occurs when the brand is considered to resemble the original brand, which makes it uncomfortable for musicians. Even though the quality of the tone produced is not assessed from a brand perspective, it is felt by brand owners to be quite a violation of the code of ethics. This has an impact on marketing products that are considered genuine. In this paper, a concept will be proposed in determining whether a trademark is considered authentic in terms of the logo. Use of Visual Computing with SIFT and SURF algorithms. SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) are two popular algorithms for feature extraction and matching in image processing. Both are frequently used in computer vision applications such as object detection, image matching, and object recognition. The results of this analysis will be used for musicians who want to buy musical equipment to be able to detect the logo to compare the success of a musical instrument product. The appropriateness of the quality itself will be discussed in a different discussion. The benefits that can be generated will make musicians, especially in Indonesia, more conscientious. The possibility of sorting so that you can like your own product will be very possible.

3D Point Cloud Dataset of Heavy Construction Equipment

Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data.

Exploring the Semantic-Inconsistency Effect in Scenes Using a Continuous Measure of Linguistic-Semantic Similarity.

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object-scene relations (which objects are expected in a given scene) and object-object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers' attention (the semantic-inconsistency effect). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs. inconsistent). In an eye-tracking experiment (N = 21 adults), we study the semantic-inconsistency effect in a continuous manner by using the linguistic-semantic similarity of an object to the scene category and to other objects in the scene. We found that both highly consistent and highly inconsistent objects are viewed more than other objects (U-shaped relationship), revealing that the (in)consistency effect is more than a simple binary classification.

Role of dopamine neurons in familiarity.

Dopamine neurons signal the salience of environmental stimuli and influence learning, although it is less clear if these neurons also determine the salience of memories. Ventral tegmental area (VTA) dopamine neurons increase their firing in the presence of new objects and reduce it upon repeated, inconsequential exposures, marking the shift from novelty to familiarity. This study investigates how dopamine neuron activity during repeated familiar object exposure affects an animal's preference for new objects in a subsequent novel object recognition (NOR) test. We hypothesize that a single familiarization session will not sufficiently lower dopamine activity, such that the memory of a familiar object remains salient, leading to equal exploration of familiar and novel objects and weaker NOR discrimination. In contrast, multiple familiarization sessions likely suppress dopamine activity more effectively, reducing the salience of the familiar object and enhancing subsequent novelty discrimination. Our experiments in mice indicated that multiple familiarization sessions reduce VTA dopamine neuron activation, as measured by c-Fos expression, and enhance novelty discrimination compared with a single familiarization session. Dopamine neurons that show responsiveness to novelty were primarily located in the paranigral nucleus of the VTA and expressed vesicular glutamate transporter 2 transcripts, marking them as dopamine-glutamate neurons. Chemogenetic inhibition of dopamine neurons during a single session paralleled the effects of multiple sessions, improving NOR. These findings suggest that a critical role of dopamine neurons during the transition from novelty to familiarity is to modulate the salience of an object's memory.

The Protective Effects of High-Intensity Interval Training Combined with Q10 Supplementation on Learning and Memory Impairments in Male Rats with Amyloid-β-Induced Alzheimer's Disease.

Oxidative stress plays a major role in the progression of Alzheimer's disease (AD)-related cognitive deficits. This study was done to determine the protective effects of coenzyme Q10 (CoQ10) and high-intensity interval training (HIIT) alone and in combination for eight continuous weeks, on oxidative status, cognitive functions, and histological changes in the hippocampus in amyloid-β (Aβ)-induced AD rats. Ninety male Wistar rats were randomly assigned to the sham, control, Q10 (50 mg/kg of CoQ10; P.O.), HIIT (high intensity: 4 min running at 85-90% VO2max, low intensity: 3 min running at 50-60% VO2max), Q10 + HIIT, AD, AD+Q10, AD+HIIT, and AD+Q10 + HIIT groups. The results showed that Aβ injection reduced cognitive functions in the Morris water maze (MWM) test and recognition memory in the novel object recognition test (NORT), which was accompanied by a decrease in total thiol groups, catalase, and glutathione peroxidase activities, an increase in malondialdehyde levels, and neuronal loss in the hippocampus. Interestingly, pretreatment with CoQ10, HIIT, or both, could markedly improve the oxidative status and cognitive decline in the MWM and NOR tests, and hinder neuronal loss in the hippocampus of Aβ-induced AD rats. Therefore, a combination of CoQ10 and HIIT can improve Aβ-related cognitive deficits, probably through an amelioration in hippocampal oxidative status and prevention of neuronal loss.

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

Uneven water-air media distribution or irregular liquid flow can cause changes in light propagation, leading to blurring and distortion of the extracted image, which presents a challenge for object recognition accuracy. To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. Then, an attention correction module for geometric lines is proposed to correct geometric lines in water-air cross-media images by comparing and sensing the marked feature points with large differences and utilizing the line similarity in positive and negative samples. Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. This completes the image restoration of object distortion under water-air cross-media. The proposed method is feasible and effective for restoring aberrated objects in water-air cross-media environments, with numerous experiments conducted on water-air cross-media image datasets.

Walnut Recognition Method for UAV Remote Sensing Images

During the process of walnut identification and counting using UAVs in hilly areas, the complex lighting conditions on the surface of walnuts somewhat affect the detection effectiveness of deep learning models. To address this issue, we proposed a lightweight walnut small object recognition method called w-YOLO. We reconstructed the feature extraction network and feature fusion network of the model to reduce the volume and complexity of the model. Additionally, to improve the recognition accuracy of walnut objects under complex lighting conditions, we adopted an attention mechanism detection layer and redesigned a set of detection heads more suitable for walnut small objects. A series of experiments showed that when identifying walnut objects in UAV remote sensing images, w-YOLO outperforms other mainstream object detection models, achieving a mean Average Precision (mAP0.5) of 97% and an F1-score of 92%, with parameters reduced by 52.3% compared to the YOLOv8s model. Effectively addressed the identification of walnut targets in Yunnan, China, under the influence of complex lighting conditions.

Revealing the mechanisms of semantic satiation with deep learning models

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.

Experience transforms crossmodal object representations in the anterior temporal lobes.

Combining information from multiple senses is essential to object recognition, core to the ability to learn concepts, make new inferences, and generalize across distinct entities. Yet how the mind combines sensory input into coherent crossmodal representations - the crossmodal binding problem - remains poorly understood. Here, we applied multi-echo fMRI across a 4-day paradigm, in which participants learned three-dimensional crossmodal representations created from well-characterized unimodal visual shape and sound features. Our novel paradigm decoupled the learned crossmodal object representations from their baseline unimodal shapes and sounds, thus allowing us to track the emergence of crossmodal object representations as they were learned by healthy adults. Critically, we found that two anterior temporal lobe structures - temporal pole and perirhinal cortex - differentiated learned from non-learned crossmodal objects, even when controlling for the unimodal features that composed those objects. These results provide evidence for integrated crossmodal object representations in the anterior temporal lobes that were different from the representations for the unimodal features. Furthermore, we found that perirhinal cortex representations were by default biased toward visual shape, but this initial visual bias was attenuated by crossmodal learning. Thus, crossmodal learning transformed perirhinal representations such that they were no longer predominantly grounded in the visual modality, which may be a mechanism by which object concepts gain their abstraction.

Protein kinase C-inhibition reduces critical weight loss and improves functional outcome after experimental subarachnoid haemorrhage

ObjectivesSubarachnoid haemorrhage (SAH) carries a high burden of morbidity and mortality. One in three patients develop vasospasm, which is associated with Delayed Cerebral Ischemia. The pathophysiology includes vasoconstrictor receptor upregulation in cerebral arteries. The protein kinase C – inhibitor RO-31-7549 reduces the expression of several vasoconstrictor receptors and normalizes cerebral blood flow in experimental SAH but functional and behavioural effects are unknown. This study was undertaken to analyse functional outcomes up to 14 days after experimental SAH. Materials and methods54 male rats were randomised to experimental SAH or sham, using the pre-chiasmatic, single injection model, and subsequent treatment or vehicle. 42 remained for final analysis. The animals were euthanized on day 14 or when reaching a humane endpoint. The primary endpoint was overall survival, defined as either spontaneous mortality or when reaching a predefined humane endpoint. The secondary outcomes were differences in the rotating pole test, weight, open field test, novel object recognition and qPCR of selected inflammatory markers. ResultsIn the vehicle group 6/15 rats reached the humane endpoint of >20 % weight loss compared to 1/14 in the treatment group. This resulted in a significant reduced risk of early euthanasia due to >20 % weight loss of HR 0.15 (0.03–0.66, p = 0.04). Furthermore, the treatment group did significantly better on the rotating pole test, RR 0.64 (0.47–0.91, p = 0.02). ConclusionRO-31-7549 improved outcomes in terms >20 % weight loss and rotating pole performance after experimental SAH and could be investigated.

Bile acid metabolism is altered in learning and memory impairment induced by chronic lead exposure

Lead is a neurotoxic contaminant that exists widely in the environment. Although lead neurotoxicity has been found to be tightly linked to gut microbiota disturbance, the effect of host metabolic disorders caused by gut microbiota disturbance on lead neurotoxicity has not been investigated. In this work, the results of new object recognition tests and Morris water maze tests showed that chronic low-dose lead exposure caused learning and memory dysfunction in mice. The results of 16 S rRNA sequencing of cecal contents and fecal microbiota transplantation showed that the neurotoxicity of lead could be transmitted through gut microbiota. The results of untargeted metabolomics and bile acid targeted metabolism analysis showed that the serum bile acid metabolism profile of lead-exposed mice was significantly changed. In addition, supplementation with TUDCA or INT-777 significantly alleviated chronic lead exposure-induced learning and memory impairment, primarily through inhibition of the NLRP3 inflammasome in the hippocampus to relieve neuroinflammation. In conclusion, our findings suggested that dysregulation of host bile acid metabolism may be one of the mechanisms of lead-induced neurotoxicity, and supplementation of specific bile acids may be a possible therapeutic strategy for lead-induced neurotoxicity.

Study on Real-Time Spatial Distance Acquisition Method of Transmission Line Based on Laser Point Cloud and Image Monitoring

This paper introduces the principle of safety distance diagnosis of transmission lines by using airborne LiDAR detection technology. This project intends to realize the effective segmentation of ground objects and non-ground objects through the point cloud automatic filtering technology considering the surface fluctuation characteristics. Then, this paper realizes the initial recognition of ground objects based on spatial dimension information. Then this paper extracts the ground object information based on the image data with high precision. The 3D nodes of transmission lines are obtained by Hough transform and block centroid algorithm, and the floor features are extracted by area growth algorithm. The plant feature is extracted by using plant vertex and boundary point. The distance between the power line and the surrounding objects can be measured, and the elevation of the wire and the surrounding environment can be judged accordingly. The algorithm is tested by using the observation data of unmanned airborne LiDAR, and the simulation results show that the proposed method is feasible. Through the identification of buildings, plants and other objects along the line, the harm degree of them on the power line is judged and the effective treatment is carried out. This ensures the safe operation of transmission lines.

Beyond visual integration: sensitivity of the temporal-parietal junction for objects, places, and faces

One important role of the TPJ is the contribution to perception of the global gist in hierarchically organized stimuli where individual elements create a global visual percept. However, the link between clinical findings in simultanagnosia and neuroimaging in healthy subjects is missing for real-world global stimuli, like visual scenes. It is well-known that hierarchical, global stimuli activate TPJ regions and that simultanagnosia patients show deficits during the recognition of hierarchical stimuli and real-world visual scenes. However, the role of the TPJ in real-world scene processing is entirely unexplored. In the present study, we first localized TPJ regions significantly responding to the global gist of hierarchical stimuli and then investigated the responses to visual scenes, as well as single objects and faces as control stimuli. All three stimulus classes evoked significantly positive univariate responses in the previously localized TPJ regions. In a multivariate analysis, we were able to demonstrate that voxel patterns of the TPJ were classified significantly above chance level for all three stimulus classes. These results demonstrate a significant involvement of the TPJ in processing of complex visual stimuli that is not restricted to visual scenes and that the TPJ is sensitive to different classes of visual stimuli with a specific signature of neuronal activations.

Application of depth feature recognition technology in foreign object recognition in distribution network monitoring video

Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network.

Anxiety-, and depression-like behavior following short-term finasteride administration is associated with impaired synaptic plasticity and cognitive behavior in male rats

Finasteride, a 5α-Reductase inhibitor, is used to treat male pattern baldness and benign prostatic hyperplasia. Several clinical studies show that chronic finasteride treatment induces persistent depression, suicidal thoughts and cognitive impairment and these symptoms are persistent even after its withdrawal. Previous results from our lab showed that repeated administration of finasteride for six days induces depression-like behavior. However, whether short-term finasteride administration induces anxiety-like behavior and memory impairment and alters synaptic plasticity are not known, which formed the basis of this study. Finasteride was administered to 2–2.5 months old male Wistar rats for six days and subjected to behavioral evaluation, biochemical estimation and synaptic plasticity assessment. Anxiety-like behavior was evaluated in the elevated plus maze (EPM), open field test (OFT), light/dark test (LDT), and novelty suppressed feeding test (NSFT), and learning and memory using novel object recognition test (NORT) and novel object location test (NOLT) and depression-like behavior in the sucrose preference test (SPT). Synaptic plasticity in the hippocampal Schaffer collateral-CA1 was evaluated using slice field potential recordings. Plasma corticosterone levels were estimated using ELISA. Finasteride administration induced anxiety-like behavior in the EPM, OFT, LDT and NSFT, and depression-like behavior in the SPT. Further, finasteride induced hippocampal dependent spatial learning and memory impairment in the NOLT. In addition, finasteride decreased basal synaptic plasticity and long-term potentiation (LTP) in the hippocampus. A trend of increased plasma corticosterone levels was observed following repeated finasteride administration. These results indicate the potential role of corticosterone and synaptic plasticity in finasteride-induced effects and further studies will pave way for the development of novel neurosteroid-based therapeutics in neuropsychiatric diseases.

Mass-Produced Skin-Inspired Piezoresistive Sensing Array with Interlocking Interface for Object Recognition.

E-skins, capable of responding to mechanical stimuli, hold significant potential in the field of robot haptics. However, it is a challenge to obtain e-skins with both high sensitivity and mechanical stability. Here, we present a bioinspired piezoresistive sensor with hierarchical structures based on polyaniline/polystyrene core-shell nanoparticles polymerized on air-laid paper. The combination of laser-etched reusable templates and sensitive materials that can be rapidly synthesized enables large-scale production. Benefiting from the substantially enlarged deformation of the hierarchical structure, the developed piezoresistive electronics exhibit a decent sensitivity of 21.67 kPa-1 and a subtle detection limit of 3.4 Pa. Moreover, an isolation layer is introduced to enhance the interface stability of the e-skin, with a fracture limit of 66.34 N/m. Furthermore, the e-skin can be seamlessly integrated onto gloves without any detachment issues. With the assistance of deep learning, it achieves a 98% accuracy rate in object recognition. We anticipate that this strategy will render e-skin with more robust interfaces and heightened sensing capabilities, offering a favorable pathway for large-scale production.

Exploring the neuropharmacological potential of empagliflozin on nootropic and scopolamine-induced amnesic model of Alzheimer’s like conditions in rats

Background Alzheimer’s disease (AD) is one of the most challenging and prevalent neurodegenerative disorder globally with a rising prevalence, characterized by progressive cognitive decline, memory loss, and behavioural changes. Current research aims to determine the nootropic and anti-amnesic effect of Empagliflozin (EMPA) against scopolamine-induced amnesia in rats, by modulating the cholinergic and N-Methyl D-Aspartate (NMDA) receptors. Methods Rats were treated once daily with an EMPA (5 and 10 mg/kg) and donepezil (2.5 mg/kg) for successive 26 days. During the final 13 days of treatment, a daily injection of scopolamine (1 mg/kg) was administered to induce cognitive deficits. Results EMPA was found to be significantly reduce escape latency, increase time spent in the target quadrant, and enhanced the number of target zone crossings in the Morris water maze (MWM) test, indicating improved spatial memory. Moreover, EMPA increased the recognition index and the number of spontaneous alternations in the novel object recognition (NOR) and Y-maze tests, respectively, suggesting enhanced memory. Discussion Interestingly doses of EMPA (5 mg/kg, 10 mg/kg) exhibited memory-enhancing effects even in the absence of scopolamine-induced impairment. Biochemical analysis revealed that EMPA elevated the levels of glutathione (GSH), a potent antioxidant, while decreasing lipid peroxidation (LPO) activity and increasing catalase (CAT) levels, indicating its antioxidative properties. Interestingly molecular docking studies revealed that EMPA fit perfectly in the active sites of M1 muscarinic acetylcholine (mACh) and NMDA receptors. These results indicated that the nootropic and antiamnesic effect of EMPA is possibly mediated via M1 and NMDA receptors and might be a remedy for AD.

Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains

To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, object recognition and grasping are two key functionalities for autonomous robots. Most state-of-the-art approaches treat object recognition and grasping as two separate problems, even though both use visual input. Furthermore, the knowledge of the robot is fixed after the training phase. In such cases, if the robot encounters new object categories, it must be retrained to incorporate new information without catastrophic forgetting. To resolve this problem, we propose a deep learning architecture with an augmented memory capacity to handle open-ended object recognition and grasping simultaneously. In particular, our approach takes multi-views of an object as input and jointly estimates pixel-wise grasp configuration as well as a deep scale- and rotation-invariant representation as output. The obtained representation is then used for open-ended object recognition through a meta-active learning technique. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings. Our approach empowers a robot to acquire knowledge about new object categories using, on average, less than five instances per category and achieve 95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$95\\%$$\\end{document} object recognition accuracy and above 91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$91\\%$$\\end{document} grasp success rate on (highly) cluttered scenarios in both simulation and real-robot experiments. A video of these experiments is available online at: https://youtu.be/n9SMpuEkOgk