pwc-archive/papers-with-abstracts huggingface dataset:
Underwater object classification combining SAS and transferred optical-to-SAS Imagery
Authors: Avi Abu, Roee Diamant
Date: 2023-04-24 00:00:00
Tasks: Classification

Combining synthetic aperture sonar (SAS) imagery with optical images for underwater object classification has the potential to overcome challenges such as water clarity, the stability of the optical image analysis platform, and strong reflections from the seabed for sonar-based classification. In this work, we propose this type of multi-modal combination to discriminate between man-made targets and objects such as rocks or litter. We offer a novel classification algorithm that overcomes the problem of intensity and object formation differences between the two modalities. To this end, we develop a novel set of geometrical shape descriptors that takes into account the geometrical relation between the objects shadow and highlight. Results from 7,052 pairs of SAS and optical images collected during several sea experiments show improved classification performance compared to the state-of-the-art for better discrimination between different types of underwater objects. For reproducibility, we share our database.
A Benchmark for Cycling Close Pass Near Miss Event Detection from Video Streams
Authors: Mingjie Li, Tharindu Rathnayake, Ben Beck, Lingheng Meng, Zijue Chen, Akansel Cosgun, Xiaojun Chang, Dana Kulić
Date: 2023-04-24 00:00:00
Tasks: Event Detection

Cycling is a healthy and sustainable mode of transport. However, interactions with motor vehicles remain a key barrier to increased cycling participation. The ability to detect potentially dangerous interactions from on-bike sensing could provide important information to riders and policy makers. Thus, automated detection of conflict between cyclists and drivers has attracted researchers from both computer vision and road safety communities. In this paper, we introduce a novel benchmark, called Cyc-CP, towards cycling close pass near miss event detection from video streams. We first divide this task into scene-level and instance-level problems. Scene-level detection asks an algorithm to predict whether there is a close pass near miss event in the input video clip. Instance-level detection aims to detect which vehicle in the scene gives rise to a close pass near miss. We propose two benchmark models based on deep learning techniques for these two problems. For training and testing those models, we construct a synthetic dataset and also collect a real-world dataset. Our models can achieve 88.13% and 84.60% accuracy on the real-world dataset, respectively. We envision this benchmark as a test-bed to accelerate cycling close pass near miss detection and facilitate interaction between the fields of road safety, intelligent transportation systems and artificial intelligence. Both the benchmark datasets and detection models will be available at https://github.com/SustainableMobility/cyc-cp to facilitate experimental reproducibility and encourage more in-depth research in the field.
Universal Domain Adaptation via Compressive Attention Matching
Authors: Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, Kun Kuang, Chao Wu
Date: 2023-04-24 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Adaptation, Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, which overemphasizes global information while ignoring the most crucial local objects in the image, resulting in limited accuracy. To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information. The proposed framework introduces a novel Compressive Attention Matching (CAM) approach to explore the core information by compressively representing attentions. Furthermore, CAM incorporates a residual-based measurement to determine the sample commonness. By utilizing the measurement, UniAM achieves domain-wise and category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS). Notably, UniAM is the first method utilizing the attention in vision transformer directly to perform classification tasks. Extensive experiments show that UniAM outperforms the current state-of-the-art methods on various benchmark datasets.
Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images
Authors: Pamela C. Zurita, Daniel P. Benalcazar, Juan E. Tapia
Date: 2023-04-24 00:00:00
Tasks: Temporal Sequences

Fitness for Duty (FFD) techniques detects whether a subject is Fit to perform their work safely, which means no reduced alertness condition and security, or if they are Unfit, which means alertness condition reduced by sleepiness or consumption of alcohol and drugs. Human iris behaviour provides valuable information to predict FFD since pupil and iris movements are controlled by the central nervous system and are influenced by illumination, fatigue, alcohol, and drugs. This work aims to classify FFD using sequences of 8 iris images and to extract spatial and temporal information using Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM). Our results achieved a precision of 81.4\% and 96.9\% for the prediction of Fit and Unfit subjects, respectively. The results also show that it is possible to determine if a subject is under alcohol, drug, and sleepiness conditions. Sleepiness can be identified as the most difficult condition to be determined. This system opens a different insight into iris biometric applications.
Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network
Authors: Rui Zhang, Luziwei Leng, Kaiwei Che, Hu Zhang, Jie Cheng, Qinghai Guo, Jiangxing Liao, Ran Cheng
Date: 2023-04-24 00:00:00
Tasks: Decoder, Event-based vision, Semantic Segmentation

Spiking neural networks (SNNs), known for their low-power, event-driven computation and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous signals from event-based sensors. Despite their potential, SNNs face challenges in training and architectural design, resulting in limited performance in challenging event-based dense prediction tasks compared to artificial neural networks (ANNs). In this work, we develop an efficient spiking encoder-decoder network (SpikingEDN) for large-scale event-based semantic segmentation tasks. To enhance the learning efficiency from dynamic event streams, we harness the adaptive threshold which improves network accuracy, sparsity and robustness in streaming inference. Moreover, we develop a dual-path Spiking Spatially-Adaptive Modulation module, which is specifically tailored to enhance the representation of sparse events and multi-modal inputs, thereby considerably improving network performance. Our SpikingEDN attains a mean intersection over union (MIoU) of 72.57\% on the DDD17 dataset and 58.32\% on the larger DSEC-Semantic dataset, showing competitive results to the state-of-the-art ANNs while requiring substantially fewer computational resources. Our results shed light on the untapped potential of SNNs in event-based vision applications. The source code will be made publicly available.
Glocal Energy-based Learning for Few-Shot Open-Set Recognition
Authors: Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang
Date: 2023-04-24 00:00:00
Proceeding: CVPR 2023 1
Tasks: Open Set Learning

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions
Authors: Yun He, Danhang Tang, yinda zhang, xiangyang xue, Yanwei Fu
Date: 2023-04-24 00:00:00
Proceeding: CVPR 2023 1
Tasks: point cloud upsampling

Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However,they usually suffer from two critical issues: (1)fixed upsampling rate after one-time training, since the feature expansion unit is customized for each upsampling rate; (2)outliers or shrinkage artifact caused by the difficulty of precisely predicting 3D coordinates or residuals of upsampled points. To adress them, we propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate. And then refine the positions of the interpolated points with an iterative optimization process, guided by a trained model estimating the difference between the current point cloud and the high-res target. Extensive quantitative and qualitative results on benchmarks and downstream tasks demonstrate that our method achieves the state-of-the-art accuracy and efficiency.
Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
Authors: Yonggan Fu, Zhifan Ye, Jiayi Yuan, Shunyao Zhang, Sixu Li, Haoran You, Yingyan Celine Lin
Date: 2023-04-24 00:00:00
Tasks: Generalizable Novel View Synthesis, NeRF, Novel View Synthesis

Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural Radiance Fields (NeRFs) have gained increasing popularity thanks to their cross-scene generalization capability. Despite their promise, the real-device deployment of generalizable NeRFs is bottlenecked by their prohibitive complexity due to the required massive memory accesses to acquire scene features, causing their ray marching process to be memory-bounded. To this end, we propose Gen-NeRF, an algorithm-hardware co-design framework dedicated to generalizable NeRF acceleration, which for the first time enables real-time generalizable NeRFs. On the algorithm side, Gen-NeRF integrates a coarse-then-focus sampling strategy, leveraging the fact that different regions of a 3D scene contribute differently to the rendered pixel, to enable sparse yet effective sampling. On the hardware side, Gen-NeRF highlights an accelerator micro-architecture to maximize the data reuse opportunities among different rays by making use of their epipolar geometric relationship. Furthermore, our Gen-NeRF accelerator features a customized dataflow to enhance data locality during point-to-hardware mapping and an optimized scene feature storage strategy to minimize memory bank conflicts. Extensive experiments validate the effectiveness of our proposed Gen-NeRF framework in enabling real-time and generalizable novel view synthesis.
Robust and Efficient Memory Network for Video Object Segmentation
Authors: Yadang Chen, Dingwei Zhang, Zhi-Xin Yang, Enhua Wu
Date: 2023-04-24 00:00:00
Tasks: Object, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently achieved outstanding VOS performance by performing non-local pixel-wise matching between the query and memory. However, these methods have two limitations. 1) Non-local matching could cause distractor objects in the background to be incorrectly segmented. 2) Memory features with high temporal redundancy consume significant computing resources. For limitation 1, we introduce a local attention mechanism that tackles the background distraction by enhancing the features of foreground objects with the previous mask. For limitation 2, we first adaptively decide whether to update the memory features depending on the variation of foreground objects to reduce temporal redundancy. Second, we employ a dynamic memory bank, which uses a lightweight and differentiable soft modulation gate to decide how many memory features need to be removed in the temporal dimension. Experiments demonstrate that our REMN achieves state-of-the-art results on DAVIS 2017, with a $\mathcal{J\&F}$ score of 86.3% and on YouTube-VOS 2018, with a $\mathcal{G}$ over mean of 85.5%. Furthermore, our network shows a high inference speed of 25+ FPS and uses relatively few computing resources.
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence
Authors: Yonggan Fu, Yuecheng Li, Chenghui Li, Jason Saragih, Peizhao Zhang, Xiaoliang Dai, Yingyan Celine Lin
Date: 2023-04-24 00:00:00
Proceeding: CVPR 2023 1
Tasks: Neural Architecture Search

Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras with a quality level that can match the realism of the avatar's human appearance. To this end, we propose a framework called Auto-CARD, which for the first time enables real-time and robust driving of Codec Avatars when exclusively using merely on-device computing resources. This is achieved by minimizing two sources of redundancy. First, we develop a dedicated neural architecture search technique called AVE-NAS for avatar encoding in AR/VR, which explicitly boosts both the searched architectures' robustness in the presence of extreme facial expressions and hardware friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal redundancy in consecutively captured images during continuous rendering and develop a mechanism dubbed LATEX to skip the computation of redundant frames. Specifically, we first identify an opportunity from the linearity of the latent space derived by the avatar decoder and then propose to perform adaptive latent extrapolation for redundant frames. For evaluation, we demonstrate the efficacy of our Auto-CARD framework in real-time Codec Avatar driving settings, where we achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even better animation quality than state-of-the-art avatar encoder designs.
Function-Consistent Feature Distillation
Authors: Dongyang Liu, Meina Kan, Shiguang Shan, Xilin Chen
Date: 2023-04-24 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Feature distillation makes the student mimic the intermediate features of the teacher. Nearly all existing feature-distillation methods use L2 distance or its slight variants as the distance metric between teacher and student features. However, while L2 distance is isotropic w.r.t. all dimensions, the neural network's operation on different dimensions is usually anisotropic, i.e., perturbations with the same 2-norm but in different dimensions of intermediate features lead to changes in the final output with largely different magnitude. Considering this, we argue that the similarity between teacher and student features should not be measured merely based on their appearance (i.e., L2 distance), but should, more importantly, be measured by their difference in function, namely how later layers of the network will read, decode, and process them. Therefore, we propose Function-Consistent Feature Distillation (FCFD), which explicitly optimizes the functional similarity between teacher and student features. The core idea of FCFD is to make teacher and student features not only numerically similar, but more importantly produce similar outputs when fed to the later part of the same network. With FCFD, the student mimics the teacher more faithfully and learns more from the teacher. Extensive experiments on image classification and object detection demonstrate the superiority of FCFD to existing methods. Furthermore, we can combine FCFD with many existing methods to obtain even higher accuracy. Our codes are available at https://github.com/LiuDongyang6/FCFD.
Shape from Shading for Robotic Manipulation
Authors: Arkadeep Narayan Chaudhury, Leonid Keselman, Christopher G. Atkeson
Date: 2023-04-24 00:00:00

Controlling illumination can generate high quality information about object surface normals and depth discontinuities at a low computational cost. In this work we demonstrate a robot workspace-scaled controlled illumination approach that generates high quality information for table top scale objects for robotic manipulation. With our low angle of incidence directional illumination approach, we can precisely capture surface normals and depth discontinuities of monochromatic Lambertian objects. We show that this approach to shape estimation is 1) valuable for general purpose grasping with a single point vacuum gripper, 2) can measure the deformation of known objects, and 3) can estimate pose of known objects and track unknown objects in the robot's workspace.
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer
Authors: Hao Tang, Songhua Liu, Tianwei Lin, Shaoli Huang, Fu Li, Dongliang He, Xinchao Wang
Date: 2023-04-24 00:00:00
Proceeding: CVPR 2023 1
Tasks: Meta-Learning, Style Transfer

Transformer-based models achieve favorable performance in artistic style transfer recently thanks to its global receptive field and powerful multi-head/layer attention operations. Nevertheless, the over-paramerized multi-layer structure increases parameters significantly and thus presents a heavy burden for training. Moreover, for the task of style transfer, vanilla Transformer that fuses content and style features by residual connections is prone to content-wise distortion. In this paper, we devise a novel Transformer model termed as \emph{Master} specifically for style transfer. On the one hand, in the proposed model, different Transformer layers share a common group of parameters, which (1) reduces the total number of parameters, (2) leads to more robust training convergence, and (3) is readily to control the degree of stylization via tuning the number of stacked layers freely during inference. On the other hand, different from the vanilla version, we adopt a learnable scaling operation on content features before content-style feature interaction, which better preserves the original similarity between a pair of content features while ensuring the stylization quality. We also propose a novel meta learning scheme for the proposed model so that it can not only work in the typical setting of arbitrary style transfer, but also adaptable to the few-shot setting, by only fine-tuning the Transformer encoder layer in the few-shot stage for one specific style. Text-guided few-shot style transfer is firstly achieved with the proposed framework. Extensive experiments demonstrate the superiority of Master under both zero-shot and few-shot style transfer settings.
NoiseTrans: Point Cloud Denoising with Transformers
Authors: Guangzhe Hou, Guihe Qin, Minghui Sun, Yanhua Liang, Jie Yan, Zhonghan Zhang
Date: 2023-04-24 00:00:00
Tasks: 3D Reconstruction, Denoising

Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream tasks. The paper aims to recover the underlying surface of noisy point clouds. We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising. Specifically, we obtain structural similarity of point-based point clouds with the assistance of the transformer's core self-attention mechanism. By expressing the noisy point cloud as a set of unordered vectors, we convert point clouds into point embeddings and employ Transformer to generate clean point clouds. To make the Transformer preserve details when sensing the point cloud, we design the Local Point Attention to prevent the point cloud from being over-smooth. In addition, we also propose sparse encoding, which enables the Transformer to better perceive the structural relationships of the point cloud and improve the denoising performance. Experiments show that our model outperforms state-of-the-art methods in various datasets and noise environments.
OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images
Authors: Xuexue Li, Wenhui Diao, Yongqiang Mao, Peng Gao, Xiuhua Mao, Xinming Li, Xian Sun
Date: 2023-04-24 00:00:00
Tasks: object-detection, Object Detection, Occlusion Estimation

Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance.
Coevolution of Camouflage
Authors: Craig Reynolds
Date: 2023-04-24 00:00:00
Tasks: Artificial Life

Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition with evolving predator vision. During their "lifetime" predators learn to better locate camouflaged prey. The environment for this 2D simulation is provided by a set of photographs, typically of natural scenes. This model is based on two evolving populations, one of prey and another of predators. Mutual conflict between these populations can produce both effective prey camouflage and predators skilled at "breaking" camouflage. The result is an open source artificial life model to help study camouflage in nature, and the perceptual phenomenon of camouflage more generally.