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Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk

Self-Point-Flow: Self-Supervised Scene Flow Estimation from ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Ruibo Lin, Guosheng Xie, Lihua Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781665445092

Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point coordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for accurate matching;and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training.

关键词： Training computer vision Three-dimensional displays Costs Image color analysis Supervised learning Estimation

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Searching for Fast Model Families on Datacenter Accelerators

Searching for Fast Model Families on Datacenter Accelerators

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Sheng Tan, Mingxing Pang, Ruoming Li, Andrew Cheng, Liqun Le, Quoc, V Jouppi, Norman P. Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than Efficient-Net (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.

关键词： Convolutional codes computer vision Convolution Computational modeling Search methods Parallel processing Hardware

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Honeybee: Locality-Enhanced Projector for Multimodal LLM

Honeybee: Locality-Enhanced Projector for Multimodal LLM

引用

conference on computer vision and pattern recognition (cvpr)

作者： Junbum Cha Wooyoung Kang Jonghwan Mun Byungseok Roh Kakao Brain

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' over-all efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://***/kakaobrain/honeybee.

关键词： Visualization computer vision Codes Large language models Benchmark testing pattern recognition Tuning

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DeepACG: Co-Saliency Detection via Semantic-aware Contrast Gromov-Wasserstein Distance

DeepACG: Co-Saliency Detection via Semantic-aware Contrast G...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Kaihua Dong, Mingliang Liu, Bo Yuan, Xiao-Tong Liu, Qingshan Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing Peoples R China JD Digits Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

The objective of co-saliency detection is to segment the co-occurring salient objects in a group of images. To address this task, we introduce a new deep network architecture via semantic-aware contrast Gromov-Wasserstein distance (DeepACG). We first adopt the Gromov-Wasserstein (GW) distance to build dense 4D correlation volumes for all pairs of image pixels within the image group. These dense correlation volumes enable the network to accurately discover the structured pair-wise pixel similarities among the common salient objects. Second, we develop a semantic-aware co-attention module (SCAM) to enhance the foreground co-saliency through predicted categorical information. Specifically, SCAM recognizes the semantic class of the foreground co-objects, and this information is then modulated to the deep representations to localize the related pixels. Third, we design a contrast edge-enhanced module (EEM) to capture richer contexts and preserve fine-grained spatial information. We validate the effectiveness of our model using three largest and most challenging benchmark datasets (Cosal2015, CoCA, and CoSOD3k). Extensive experiments have demonstrated the substantial practical merit of each module. Compared with the existing works, DeepACG shows significant improvements and achieves state-of-the-art performance.

关键词： Image segmentation computer vision Correlation Image edge detection Semantics Network architecture Benchmark testing

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization

VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aw...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Choi, Seunghwan Park, Sunghyun Lee, Minsoo Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped clothes and the desired clothing regions become noticeable in the final results;the architectures used in existing methods have low performance in generating high-quality body parts and maintaining the texture sharpness of the clothes. To address the challenges, we propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. Specifically, we first prepare the segmentation map to guide our virtual try-on synthesis, and then roughly fit the target clothing item to a given person's body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS generator to handle the misaligned areas and preserve the details of 1024x768 inputs. Through rigorous comparison with existing methods, we demonstrate that VITON-HD highly surpasses the baselines in terms of synthesized image quality both qualitatively and quantitatively.

关键词： Image quality Image segmentation computer vision Image resolution Clothing Semantics Fitting

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Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dy...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yao, Lewei Pi, Renjie Xu, Hang Zhang, Wei Li, Zhenguo Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphism optimizes the student's architecture and removes the redundant parameters, while dynamic distillation aims to find the optimal matching teacher. For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search;To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches. Given a base detector as the input, our algorithm directly outputs the derived student detector with high performance without additional training. Experiments demonstrate that our Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par with the SOTA EfficientDet while requiring less search cost. We hope our proposed method can provide the community with a new way of jointly optimizing NAS, KD and pruning.

关键词： Training Costs Heuristic algorithms Detectors Object detection computer architecture Search problems

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Prototype Augmentation and Self-Supervision for Incremental Learning

Prototype Augmentation and Self-Supervision for Incremental ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Fei Zhang, Xu-Yao Wang, Chuang Yin, Fei Liu, Cheng-Lin Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781665445092

Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data or complex generative models. However, storing data from previous tasks is limited by memory or privacy issues, and generative models are usually unstable and inefficient in training. In this paper, we propose a simple non-exemplar based method named PASS, to address the catastrophic forgetting problem in incremental learning. On the one hand, we propose to memorize one class-representative prototype for each old class and adopt prototype augmentation (protoAug) in the deep feature space to maintain the decision boundary of previous tasks. On the other hand, we employ self:supervised learning (SSL) to learn more generalizable and transferable features for other tasks, which demonstrates the effectiveness of SSL in incremental learning. Experimental results on benchmark datasets show that our approach significantly outperforms non-exemplar based methods, and achieves comparable performance compared to exemplar based approaches.

关键词： Training Learning systems Deep learning Data privacy computer vision Computational modeling Prototypes

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S²-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

S<SUP>2</SUP>-BNN: Bridging the Gap Between Self-Supervised ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shen, Zhiqiang Liu, Zechun Qin, Jie Huang, Lei Cheng, Kwang-Ting Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet been fully explored in the community. In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels. We observe that the commonly used contrastive objective is not satisfying on BNNs for competitive accuracy, since the backbone network contains relatively limited capacity and representation ability. Hence instead of directly applying existing self-supervised methods, which cause a severe decline in performance, we present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution, to minimize the loss and obtain desirable accuracy. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.5 similar to 15% on BNNs. We further reveal that it is difficult for BNNs to recover the similar predictive distributions as real-valued models when training without labels. Thus, how to calibrate them is key to address the degradation in performance. Extensive experiments are conducted on the large-scale ImageNet and downstream datasets. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.

关键词： Training Degradation computer vision Codes Neural networks Supervised learning Predictive models

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Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Rethinking Class Relations: Absolute-relative Supervised and...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781665445092

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets.

关键词： Training Learning systems computer vision Protocols Annotations Animals Semantics

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