pwc-archive/papers-with-abstracts huggingface dataset:
Source-Free Adaptive Gaze Estimation by Uncertainty Reduction
Authors: Xin Cai, Jiabei Zeng, Shiguang Shan, Xilin Chen
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Domain Adaptation, Gaze Estimation, Source-Free Domain Adaptation

    Gaze estimation across domains has been explored recently because the training data are usually collected under controlled conditions while the trained gaze estimators are used in real and diverse environments. However, due to privacy and efficiency concerns, simultaneous access to annotated source data and to-be-predicted target data can be challenging. In light of this, we present an unsupervised source-free domain adaptation approach for gaze estimation, which adapts a source-trained gaze estimator to unlabeled target domains without source data. We propose the Uncertainty Reduction Gaze Adaptation (UnReGA) framework, which achieves adaptation by reducing both sample and model uncertainty. Sample uncertainty is mitigated by enhancing image quality and making them gaze-estimation-friendly, whereas model uncertainty is reduced by minimizing prediction variance on the same inputs. Extensive experiments are conducted on six cross-domain tasks, demonstrating the effectiveness of UnReGA and its components. Results show that UnReGA outperforms other state-of-the-art cross-domain gaze estimation methods under both protocols, with and without source data
Tree Instance Segmentation With Temporal Contour Graph
Authors: Adnan Firoze, Cameron Wingren, Raymond A. Yeh, Bedrich Benes, Daniel Aliaga
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Benchmarking, Instance Segmentation, Segmentation, Semantic Segmentation

    We present a novel approach to perform instance segmentation, and counting, for densely packed self-similar trees using a top-view RGB image sequence. We propose a solution that leverages pixel content, shape, and self-occlusion. First, we perform an initial over-segmentation of the image sequence and aggregate structural characteristics into a contour graph with temporal information incorporated. Second, using a graph convolutional network and its inherent local messaging passing abilities, we merge adjacent tree crown patches into a final set of tree crowns. Per various studies and comparisons, our method is superior to all prior methods and results in high-accuracy instance segmentation and counting, despite the trees being tightly packed. Finally, we provide various forest image sequence datasets suitable for subsequent benchmarking and evaluation captured at different altitudes and leaf conditions.
Transformer-Based Unified Recognition of Two Hands Manipulating Objects
Authors: Hoseong Cho, Chanwoo Kim, Jihyeon Kim, Seongyeong Lee, Elkhan Ismayilzada, Seungryul Baek
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Action Recognition, Object

    Understanding the hand-object interactions from an egocentric video has received a great attention recently. So far, most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to provide the unified understanding of two hands, an object and their interactions. In this paper, we propose the Transformer-based unified framework that provides better understanding of two hands manipulating objects. In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types. Afterwards, the action class defined by the hand-object interactions is predicted from the entire video based on the estimated information combined with the contact map that encodes the interaction between two hands and an object. Experiments are conducted on H2O and FPHA benchmark datasets and we demonstrated the superiority of our method achieving the state-of-the-art accuracy. Ablative studies further demonstrate the effectiveness of each proposed module.
Weak-Shot Object Detection Through Mutual Knowledge Transfer
Authors: Xuanyi Du, Weitao Wan, Chong Sun, Chen Li
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Multiple Instance Learning, Object, object-detection, Object Detection, Transfer Learning

    Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity.
CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning
Authors: Jianlong Wu, Haozhe Yang, Tian Gan, Ning Ding, Feijun Jiang, Liqiang Nie
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Contrastive Learning

    The recently proposed FixMatch and FlexMatch have achieved remarkable results in the field of semi-supervised learning. But these two methods go to two extremes as FixMatch and FlexMatch use a pre-defined constant threshold for all classes and an adaptive threshold for each category, respectively. By only investigating consistency regularization, they also suffer from unstable results and indiscriminative feature representation, especially under the situation of few labeled samples. In this paper, we propose a novel CHMatch method, which can learn robust adaptive thresholds for instance-level prediction matching as well as discriminative features by contrastive hierarchical matching. We first present a memory-bank based robust threshold learning strategy to select highly-confident samples. In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning. CHMatch achieves very stable and superior results on several commonly-used benchmarks. For example, CHMatch achieves 8.44% and 9.02% error rate reduction over FlexMatch on CIFAR-100 under WRN-28-2 with only 4 and 25 labeled samples per class, respectively.
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution
Authors: Yixuan Sun, Dongyang Zhao, Zhangyue Yin, Yiwen Huang, Tao Gui, Wenqiang Zhang, Weifeng Ge
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Super-Resolution

    This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://github.com/YXSUNMADMAX/ACTR.
Robust 3D Shape Classification via Non-Local Graph Attention Network
Authors: Shengwei Qin, Zhong Li, Ligang Liu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Shape Classification, Classification, Graph Attention, Robust classification

    We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods.
Learning Compact Representations for LiDAR Completion and Generation
Authors: Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, Raquel Urtasun
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To address these issues, we present UltraLiDAR, a data-driven framework for scene-level LiDAR completion, LiDAR generation, and LiDAR manipulation. The crux of UltraLiDAR is a compact, discrete representation that encodes the point cloud's geometric structure, is robust to noise, and is easy to manipulate. We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost. Furthermore, by learning a prior over the discrete codebook, we can generate diverse, realistic LiDAR point clouds for self-driving. We evaluate the effectiveness of UltraLiDAR on sparse-to-dense LiDAR completion and LiDAR generation. Experiments show that densifying real-world point clouds with our approach can significantly improve the performance of downstream perception systems. Compared to prior art on LiDAR generation, our approach generates much more realistic point clouds. According to A/B test, over 98.5% of the time human participants prefer our results over those of previous methods. Please refer to project page https://waabi.ai/research/ultralidar/ for more information.
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object Detection
Authors: Jinsheng Xiao, Yuanxu Wu, Yunhua Chen, Shurui Wang, Zhongyuan Wang, Jiayi Ma
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Object, object-detection, Object Detection, Small Object Detection

    Video small object detection is a difficult task due to the lack of object information. Recent methods focus on adding more temporal information to obtain more potent high-level features, which often fail to specify the most vital information for small objects, resulting in insufficient or inappropriate features. Since information from frames at different positions contributes differently to small objects, it is not ideal to assume that using one universal method will extract proper features. We find that context information from the long-term frame and temporal information from the short-term frame are two useful cues for video small object detection. To fully utilize these two cues, we propose a long short-term feature enhancement network (LSTFE-Net) for video small object detection. First, we develop a plug-and-play spatio-temporal feature alignment module to create temporal correspondences between the short-term and current frames. Then, we propose a frame selection module to select the long-term frame that can provide the most additional context information. Finally, we propose a long short-term feature aggregation module to fuse long short-term features. Compared to other state-of-the-art methods, our LSTFE-Net achieves 4.4% absolute boosts in AP on the FL-Drones dataset. More details can be found at https://github.com/xiaojs18/LSTFE-Net.
Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space
Authors: Tianxin Huang, Zhonggan Ding, Jiangning Zhang, Ying Tai, Zhenyu Zhang, Mingang Chen, Chengjie Wang, Yong liu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Point cloud reconstruction

    For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations.
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
Authors: Dongshuo Yin, Yiran Yang, Zhechao Wang, Hongfeng Yu, Kaiwen Wei, Xian Sun
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

    Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.
VideoTrack: Learning To Track Objects via Video Transformer
Authors: Fei Xie, Lei Chu, Jiahao Li, Yan Lu, Chao Ma
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Triplet, Visual Tracking

    Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments. In this work, we resort to sequence-level target matching that can encode temporal contexts into the spatial features through a neat feedforward video model. Specifically, we adapt the standard video transformer architecture to visual tracking by enabling spatiotemporal feature learning directly from frame-level patch sequences. To better adapt to the tracking task, we carefully blend the spatiotemporal information in the video clips through sequential multi-branch triplet blocks, which formulates a video transformer backbone. Our experimental study compares different model variants, such as tokenization strategies, hierarchical structures, and video attention schemes. Then, we propose a disentangled dual-template mechanism that decouples static and dynamic appearance changes over time, and reduces the temporal redundancy in video frames. Extensive experiments show that our method, named as VideoTrack, achieves state-of-the-art results while running in real-time.
Object-Goal Visual Navigation via Effective Exploration of Relations Among Historical Navigation States
Authors: Heming Du, Lincheng Li, Zi Huang, Xin Yu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: State Estimation, valid, Visual Navigation

    Object-goal visual navigation aims at steering an agent toward an object via a series of moving steps. Previous works mainly focus on learning informative visual representations for navigation, but overlook the impacts of navigation states on the effectiveness and efficiency of navigation. We observe that high relevance among navigation states will cause navigation inefficiency or failure for existing methods. In this paper, we present a History-inspired Navigation Policy Learning (HiNL) framework to estimate navigation states effectively by exploring relationships among historical navigation states. In HiNL, we propose a History-aware State Estimation (HaSE) module to alleviate the impacts of dominant historical states on the current state estimation. Meanwhile, HaSE also encourages an agent to be alert to the current observation changes, thus enabling the agent to make valid actions. Furthermore, we design a History-based State Regularization (HbSR) to explicitly suppress the correlation among navigation states in training. As a result, our agent can update states more effectively while reducing the correlations among navigation states. Experiments on the artificial platform AI2-THOR (i.e.,, iTHOR and RoboTHOR) demonstrate that HiNL significantly outperforms state-of-the-art methods on both Success Rate and SPL in unseen testing environments.
Deep Semi-Supervised Metric Learning With Mixed Label Propagation
Authors: Furen Zhuang, Pierre Moulin
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Information Retrieval, Metric Learning, Retrieval

    Metric learning requires the identification of far-apart similar pairs and close dissimilar pairs during training, and this is difficult to achieve with unlabeled data because pairs are typically assumed to be similar if they are close. We present a novel metric learning method which circumvents this issue by identifying hard negative pairs as those which obtain dissimilar labels via label propagation (LP), when the edge linking the pair of data is removed in the affinity matrix. In so doing, the negative pairs can be identified despite their proximity, and we are able to utilize this information to significantly improve LP's ability to identify far-apart positive pairs and close negative pairs. This results in a considerable improvement in semi-supervised metric learning performance as evidenced by recall, precision and Normalized Mutual Information (NMI) performance metrics on Content-based Information Retrieval (CBIR) applications.
Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder
Authors: Aming Wu, Cheng Deng
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Object, object-detection, Object Detection, Object Localization

    Discriminating known from unknown objects is an important essential ability for human beings. To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors. Due to lacking unknown data for supervision, for this task, the main challenge lies in how to leverage the known in-distribution (ID) data to improve the detector's discrimination ability. In this paper, we first propose a method of Structure-Enhanced Recurrent Variational AutoEncoder (SR-VAE), which mainly consists of two dedicated recurrent VAE branches. Specifically, to boost the performance of object localization, we explore utilizing the classical Laplacian of Gaussian (LoG) operator to enhance the structure information in the extracted low-level features. Meanwhile, we design a VAE branch that recurrently generates the augmentation of the classification features to strengthen the discrimination ability of the object classifier. Finally, to alleviate the impact of lacking unknown data, another cycle-consistent conditional VAE branch is proposed to synthesize virtual OOD features that deviate from the distribution of ID features, which improves the capability of distinguishing OOD objects. In the experiments, our method is evaluated on OOD-OD, open-vocabulary detection, and incremental object detection. The significant performance gains over baselines show the superiorities of our method. The code will be released at https://github.com/AmingWu/SR-VAE.
Adaptive Global Decay Process for Event Cameras
Authors: Urbano Miguel Nunes, Ryad Benosman, Sio-Hoi Ieng
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Event-based vision

    In virtually all event-based vision problems, there is the need to select the most recent events, which are assumed to carry the most relevant information content. To achieve this, at least one of three main strategies is applied, namely: 1) constant temporal decay or fixed time window, 2) constant number of events, and 3) flow-based lifetime of events. However, these strategies suffer from at least one major limitation each. We instead propose a novel decay process for event cameras that adapts to the global scene dynamics and whose latency is in the order of nanoseconds. The main idea is to construct an adaptive quantity that encodes the global scene dynamics, denoted by event activity. The proposed method is evaluated in several event-based vision problems and datasets, consistently improving the corresponding baseline methods' performance. We thus believe it can have a significant widespread impact on event-based research. Code available: https://github.com/neuromorphic-paris/event_batch.