pwc-archive/papers-with-abstracts huggingface dataset:
Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN Models
Authors: Nilesh Ahuja, Parual Datta, Bhavya Kanzariya, V. Srinivasa Somayazulu, Omesh Tickoo
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: image-classification, Image Classification, Semantic Segmentation

    Thanks to advances in computer vision and AI, there has been a large growth in the demand for cloud-based visual analytics in which images captured by a low-powered edge device are transmitted to the cloud for analytics. Use of conventional codecs (JPEG, MPEG, HEVC, etc.) for compressing such data introduces artifacts that can seriously degrade the performance of the downstream analytic tasks. Split-DNN computing has emerged as a paradigm to address such usages, in which a DNN is partitioned into a client-side portion and a server side portion. Low-complexity neural networks called 'bottleneck units' are introduced at the split point to transform the intermediate layer features into a lower-dimensional representation better suited for compression and transmission. Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features. Most works on compression for image analytics use heuristic approaches to estimate the rate, leading to suboptimal performance. We propose a high-quality 'neural rate-estimator' to address this gap. We interpret the lower-dimensional bottleneck output as a latent representation of the intermediate feature and cast the rate-distortion optimization problem as one of training an equivalent variational auto-encoder with an appropriate loss function. We show that this leads to improved rate-distortion outcomes. We further show that replacing supervised loss terms (such as cross-entropy loss) by distillation-based losses in a teacher-student framework allows for unsupervised training of bottleneck units without the need for explicit training labels. This makes our method very attractive for real world deployments where access to labeled training data is difficult or expensive. We demonstrate that our method outperforms several state-of-the-art methods by obtaining improved task accuracy at lower bitrates on image classification and semantic segmentation tasks.
Relational Space-Time Query in Long-Form Videos
Authors: Xitong Yang, Fu-Jen Chu, Matt Feiszli, Raghav Goyal, Lorenzo Torresani, Du Tran
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Form, Video Understanding

    Egocentric videos are often available in the form of uninterrupted, uncurated long videos capturing the camera wearers' daily life activities.Understanding these videos requires models to be able to reason about activities, objects, and their interactions. However, current video benchmarks study these problems independently and under short, curated clips. In contrast, real-world applications, e.g., AR assistants, require bundling these problems for both model development and evaluation. In this paper, we propose to study these problems in a joint framework for long video understanding. Our contributions are three-fold. First, we propose an integrated framework, namely Relational Space-Time Query (ReST), for evaluating video understanding models via templated spatiotemporal queries. Second, we introduce two new benchmarks, ReST-ADL and ReST-Ego4D, which augment the existing egocentric video datasets with abundant query annotations generated by the ReST framework. Finally, we present a set of baselines and in-depth analysis on the two benchmarks and provide insights about the query tasks. We view our integrated framework and benchmarks as a step towards comprehensive, multi-step reasoning in long videos, and believe it will facilitate the development of next generations of video understanding models.
Learning Optical Expansion From Scale Matching
Authors: Han Ling, Yinghui Sun, Quansen Sun, Zhenwen Ren
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Optical Flow Estimation

    This paper address the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code will be available at https://github.com/HanLingsgjk/TPCV.
Unpaired Image-to-Image Translation With Shortest Path Regularization
Authors: Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image-to-Image Translation, Translation

    Unpaired image-to-image translation aims to learn proper mappings that can map images from one domain to another domain while preserving the content of the input image. However, with large enough capacities, the network can learn to map the inputs to any random permutation of images in another domain. Existing methods treat two domains as discrete and propose different assumptions to address this problem. In this paper, we start from a different perspective and consider the paths connecting the two domains. We assume that the optimal path length between the input and output image should be the shortest among all possible paths. Based on this assumption, we propose a new method to allow generating images along the path and present a simple way to encourage the network to find the shortest path without pair information. Extensive experiments on various tasks demonstrate the superiority of our approach.
GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection
Authors: Xixi Liu, Yaroslava Lochman, Christopher Zach
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Out-of-Distribution Detection

    Out-of-distribution (OOD) detection has been extensively studied in order to successfully deploy neural networks, in particular, for safety-critical applications. Moreover, performing OOD detection on large-scale datasets is closer to reality, but is also more challenging. Several approaches need to either access the training data for score design or expose models to outliers during training. Some post-hoc methods are able to avoid the aforementioned constraints, but are less competitive. In this work, we propose Generalized ENtropy score (GEN), a simple but effective entropy-based score function, which can be applied to any pre-trained softmax-based classifier. Its performance is demonstrated on the large-scale ImageNet-1k OOD detection benchmark. It consistently improves the average AUROC across six commonly-used CNN-based and visual transformer classifiers over a number of state-of-the-art post-hoc methods. The average AUROC improvement is at least 3.5%. Furthermore, we used GEN on top of feature-based enhancing methods as well as methods using training statistics to further improve the OOD detection performance. The code is available at: https://github.com/XixiLiu95/GEN.
Seeing With Sound: Long-range Acoustic Beamforming for Multimodal Scene Understanding
Authors: PRANEETH CHAKRAVARTHULA, Jim Aldon D’Souza, Ethan Tseng, Joe Bartusek, Felix Heide
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Autonomous Vehicles, object-detection, Object Detection, Scene Understanding

    Existing autonomous vehicles primarily use sensors that rely on electromagnetic waves which are undisturbed in good environmental conditions but can suffer in adverse scenarios, such as low light or for objects with low reflectance. Moreover, only objects in direct line-of-sight are typically detected by these existing methods. Acoustic pressure waves emanating from road users do not share these limitations. However, such signals are typically ignored in automotive perception because they suffer from low spatial resolution and lack directional information. In this work, we introduce long-range acoustic beamforming of pressure waves from noise directly produced by automotive vehicles in-the-wild as a  complementary sensing modality  to traditional optical sensor approaches for detection of objects in dynamic traffic environments. To this end, we introduce the first multimodal long-range acoustic beamforming dataset. We propose a neural aperture expansion method for beamforming and we validate its utility for multimodal automotive object detection. We validate the benefit of adding sound detections to existing RGB cameras in challenging automotive scenarios, where camera-only approaches fail or do not deliver the ultra-fast rates of pressure sensors.
Slimmable Dataset Condensation
Authors: Songhua Liu, Jingwen Ye, Runpeng Yu, Xinchao Wang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Dataset Condensation, Dataset Distillation

    Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt an significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained Rewards
Authors: Mingyang Sun, Mengchen Zhao, Yaqing Hou, Minglei Li, Huang Xu, Songcen Xu, Jianye Hao
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: reinforcement-learning, Reinforcement Learning (RL)

    There is a growing demand of automatically synthesizing co-speech gestures for virtual characters. However, it remains a challenge due to the complex relationship between input speeches and target gestures. Most existing works focus on predicting the next gesture that fits the data best, however, such methods are myopic and lack the ability to plan for future gestures. In this paper, we propose a novel reinforcement learning (RL) framework called RACER to generate sequences of gestures that maximize the overall satisfactory. RACER employs a vector quantized variational autoencoder to learn compact representations of gestures and a GPT-based policy architecture to generate coherent sequence of gestures autoregressively. In particular, we propose a contrastive pre-training approach to calculate the rewards, which integrates contextual information into action evaluation and successfully captures the complex relationships between multi-modal speech-gesture data. Experimental results show that our method significantly outperforms existing baselines in terms of both objective metrics and subjective human judgements. Demos can be found at https://github.com/RLracer/RACER.git.
Implicit Surface Contrastive Clustering for LiDAR Point Clouds
Authors: Zaiwei Zhang, Min Bai, Erran Li
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Object Detection, Clustering, Contrastive Learning, object-detection, Object Detection, Semantic Segmentation, Surface Reconstruction, Transfer Learning

    Self-supervised pretraining on large unlabeled datasets has shown tremendous success on improving the task performance of many computer vision tasks. However, such techniques have not been widely used for outdoor LiDAR point cloud perception due to its scene complexity and wide range. This prevents impactful application from 2D pretraining frameworks. In this paper, we propose ISCC, a new self-supervised pretraining method, core of which are two pretext tasks newly designed for LiDAR point clouds. The first task focuses on learning semantic information by sorting local groups of points in the scene into a globally consistent set of semantically meaningful clusters using contrastive learning. This is augmented with a second task which reasons about precise surfaces of various parts of the scene through implicit surface reconstruction to learn geometric structures. We demonstrate their effectiveness on transfer learning performance on 3D object detection and semantic segmentation in real world LiDAR scenes. We further design an unsupervised semantic grouping task to showcase the highly semantically meaningful features learned by our approach.
Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation With Exemplars
Authors: Taoseef Ishtiak, Qing En, Yuhong Guo
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation, Unsupervised Instance Segmentation

    Instance segmentation seeks to identify and segment each object from images, which often relies on a large number of dense annotations for model training. To alleviate this burden, unsupervised instance segmentation methods have been developed to train class-agnostic instance segmentation models without any annotation. In this paper, we propose a novel unsupervised instance segmentation approach, Exemplar-FreeSOLO, to enhance unsupervised instance segmentation by exploiting a limited number of unannotated and unsegmented exemplars. The proposed framework offers a new perspective on directly perceiving top-down information without annotations. Specifically, Exemplar-FreeSOLO introduces a novel exemplarknowledge abstraction module to acquire beneficial top-down guidance knowledge for instances using unsupervised exemplar object extraction. Moreover, a new exemplar embedding contrastive module is designed to enhance the discriminative capability of the segmentation model by exploiting the contrastive exemplar-based guidance knowledge in the embedding space. To evaluate the proposed ExemplarFreeSOLO, we conduct comprehensive experiments and perform in-depth analyses on three image instance segmentation datasets. The experimental results demonstrate that the proposed approach is effective and outperforms the state-of-the-art methods.
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation
Authors: Xiwen Wei, Zhen Xu, Cheng Liu, Si Wu, Zhiwen Yu, Hau San Wong
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Attribute, Image Manipulation, Semantic Similarity, Semantic Textual Similarity

    Great progress has been made in StyleGAN-based image editing. To associate with preset attributes, most existing approaches focus on supervised learning for semantically meaningful latent space traversal directions, and each manipulation step is typically determined for an individual attribute. To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image. Specifically, we adopt a two-stage architecture for a latent mapping network to break down the transformation process into two manageable steps. Our network first learns a diverse set of semantic directions tailored to an input image, and later nonlinearly fuses the ones associated with the target attributes to infer a residual vector. The resulting tightly interlinked two-stage architecture delivers the flexibility to handle diverse attribute combinations. By leveraging the cross-modal text-image representation of CLIP, we can perform pseudo annotations based on the semantic similarity between preset attribute text descriptions and training images, and further jointly train an auxiliary attribute classifier with the latent mapping network to provide semantic guidance. We perform extensive experiments to demonstrate that the adopted strategies contribute to the superior performance of TUSLT.
Leverage Interactive Affinity for Affordance Learning
Authors: Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, DaCheng Tao
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Human-Object Interaction Detection, Object

    Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e., extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net.
MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure Recognition
Authors: Ronald Xie, Kuan Pang, Gary D. Bader, Bo wang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Representation Learning, Segmentation

    Accurate segmentation of cellular images remains an elusive task due to the intrinsic variability in morphology of biological structures. Complete manual segmentation is unfeasible for large datasets, and while supervised methods have been proposed to automate segmentation, they often rely on manually generated ground truths which are especially challenging and time consuming to generate in biology due to the requirement of domain expertise. Furthermore, these methods have limited generalization capacity, requiring additional manual labels to be generated for each dataset and use case. We introduce MAESTER (Masked AutoEncoder guided SegmenTation at pixEl Resolution), a self-supervised method for accurate, subcellular structure segmentation at pixel resolution. MAESTER treats segmentation as a representation learning and clustering problem. Specifically, MAESTER learns semantically meaningful token representations of multi-pixel image patches while simultaneously maintaining a sufficiently large field of view for contextual learning. We also develop a cover-and-stride inference strategy to achieve pixel-level subcellular structure segmentation. We evaluated MAESTER on a publicly available volumetric electron microscopy (VEM) dataset of primary mouse pancreatic islets beta cells and achieved upwards of 29.1% improvement over state-of-the-art under the same evaluation criteria. Furthermore, our results are competitive against supervised methods trained on the same tasks, closing the gap between self-supervised and supervised approaches. MAESTER shows promise for alleviating the critical bottleneck of ground truth generation for imaging related data analysis and thereby greatly increasing the rate of biological discovery.
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers
Authors: Defu Cao, Zhaowen Wang, Jose Echevarria, Yan Liu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Quantization, Representation Learning, Retrieval, Vector Graphics

    Advances in representation learning have led to great success in understanding and generating data in various domains. However, in modeling vector graphics data, the pure data-driven approach often yields unsatisfactory results in downstream tasks as existing deep learning methods often require the quantization of SVG parameters and cannot exploit the geometric properties explicitly. In this paper, we propose a transformer-based representation learning model (SVGformer) that directly operates on continuous input values and manipulates the geometric information of SVG to encode outline details and long-distance dependencies. SVGfomer can be used for various downstream tasks: reconstruction, classification, interpolation, retrieval, etc. We have conducted extensive experiments on vector font and icon datasets to show that our model can capture high-quality representation information and outperform the previous state-of-the-art on downstream tasks significantly.
Latency Matters: Real-Time Action Forecasting Transformer
Authors: Harshayu Girase, Nakul Agarwal, Chiho Choi, Karttikeya Mangalam
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications. RAFTformer is a two-stage fully transformer based architecture which consists of a video transformer backbone that operates on high resolution, short range clips and a head transformer encoder that temporally aggregates information from multiple short range clips to span a long-term horizon. Additionally, we propose a self-supervised shuffled causal masking scheme to improve model generalization during training. Finally, we also propose a real-time evaluation setting that directly couples model inference latency to overall forecasting performance and brings forth an hitherto overlooked trade-off between latency and action forecasting performance. Our parsimonious network design facilitates RAFTformer inference latency to be 9x smaller than prior works at the same forecasting accuracy. Owing to its two-staged design, RAFTformer uses 94% less training compute and 90% lesser training parameters to outperform prior state-of-the-art baselines by 4.9 points on EGTEA Gaze+ and by 1.4 points on EPIC-Kitchens-100 dataset, as measured by Top-5 recall (T5R) in the offline setting. In the real-time setting, RAFTformer outperforms prior works by an even greater margin of upto 4.4 T5R points on the EPIC-Kitchens-100 dataset. Project Webpage: https://karttikeya.github.io/publication/RAFTformer/