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MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence

MISC210K: A Large-Scale Dataset for Multi-Instance Semantic ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yixuan Sun Yiwen Huang Haijing Guo Yuzhou Zhao Runmin Wu Yizhou Yu Weifeng Ge Wenqiang Zhang Academy of Engineering & Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China The University of Hong Kong Hong Kong China

Semantic correspondence have built up a new way for object recognition. However current single-object matching schema can be hard for discovering commonalities for a category and far from the real-world recognition tasks. To fill this gap, we design the multi-instance semantic correspondence task which aims at constructing the correspondence between multiple objects in an image pair. To support this task, we build a multi-instance semantic correspondence (MISC) dataset from COCO Detection 2017 task called MISC210K. We construct our dataset as three steps: (1) category selection and data cleaning; (2) keypoint design based on 3D models and object description rules; (3) human-machine collaborative annotation. Following these steps, we select 34 classes of objects with 4,812 challenging images annotated via a well designed semi-automatic workflow, and finally acquire 218,179 image pairs with instance masks and instance-level keypoint pairs annotated. We design a dual-path collaborative learning pipeline to train instance-level co-segmentation task and fine-grained level correspondence task together. Benchmark evaluation and further ablation results with detailed analysis are provided with three future directions proposed. Our project is available on https://***/YXSUNMADMAX/MISC210K.

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LINe: Out-of-Distribution Detection by Leveraging Important Neurons

LINe: Out-of-Distribution Detection by Leveraging Important ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Hyun Ahn Gyeong-Moon Park Seong Tae Kim Department of Artificial Intelligence Kyung Hee University Department of Computer Science and Engineering Kyung Hee University

It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of. Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specific features equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and ImageNet datasets. Code is available on https://***/LINe-OOD

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Privacy-preserving Adversarial Facial Features

Privacy-preserving Adversarial Facial Features

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conference on computer vision and pattern recognition (CVPR)

作者： Zhibo Wang He Wang Shuaifan Jin Wenwen Zhang Jiahui Hut Yan Wang Peng Sun Wei Yuan Kaixin Liu Kui Rent School of Cyber Science and Technology Zhejiang University P. R. China ZJU-Hangzhou Global Scientific and Technological Innovation Center Alibaba Group P. R. China College of Computer Science and Electronic Engineering Hunan University P. R. China School of Cyber Science and Engineering Wuhan University P. R. China

Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such features can still be exploited to recover the appearance of the original face by building a reconstruction network. Although sev-eral privacy-preserving methods have been proposed, the enhancement offace privacy protection is at the expense of accuracy degradation. In this paper, we propose an adver-sarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks. To this end, we design a shadow model which simulates the attackers' behavior to capture the mapping function from facial features to images and generate adversarial la-tent noise to disrupt the mapping. The adversarial features rather than the original features are stored in the server's database to prevent leaked features from exposing facial information. Moreover, the AdvFace requires no changes to the face recognition network and can be implemented as a privacy-enhancing plugin in deployed face recognition systems. Extensive experimental results demonstrate that Adv Face outperforms the state-of-the-art face privacy-preserving methods in defending against reconstruction at-tacks while maintaining face recognition accuracy.

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DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks

DropMAE: Masked Autoencoders with Spatial-Attention Dropout ...

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conference on computer vision and pattern recognition (CVPR)

作者： Qiangqiang Wu Tianyu Yang Ziquan Liu Baoyuan Wu Ying Shan Antoni B. Chan Department of Computer Science City University of Hong Kong International Digital Economy Academy School of Data Science The Chinese University of Hong Kong Shenzhen Tencent AI Lab

In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS. To alleviate this problem, we propose DropMAE, which adaptively performs spatial-attention dropout in the frame reconstruction to facilitate temporal correspondence learning in videos. We show that our DropMAE is a strong and efficient temporal matching learner, which achieves better finetuning results on matching-based tasks than the ImageNet-based MAE with $2\times$ faster pre-training speed. Moreover, we also find that motion diversity in pre-training videos is more important than scene diversity for improving the performance on VOT and VOS. Our pre-trained DropMAE model can be directly loaded in existing ViT-based trackers for fine-tuning without further modifications. Notably, DropMAE sets new state-of-the-art performance on 8 out of 9 highly competitive video tracking and segmentation datasets. Our code and pre-trained models are available at https://***/jimmy-dq/***.

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@ CREPE: Can vision-Language Foundation Models Reason Compositionally?

@ CREPE: Can Vision-Language Foundation Models Reason Compos...

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conference on computer vision and pattern recognition (CVPR)

作者： Zixian Ma Jerry Hong Mustafa Omer Gul Mona Gandhi Irena Gao Ranjay Krishna Stanford University Cornell University University of Pennsylvania University of Washington

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that-across 7 architectures trained with 4 algorithms on massive datasets-they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, @ CREPE, which measures two important aspects of compositionality identified by cognitive science literature: system-aticity and productivity. To measure systematicity, CREPE consists of a test dataset containing over 370K image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate 325K, 316K, and 309K hard negative captions for a subset of the pairs. To test productivity, CREPE contains 17 K image-text pairs with nine different complexities plus 278K hard negative captions with atomic, swapping and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to 9%. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size.

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Deep Incomplete Multi-View Clustering with Cross-View Partial Sample and Prototype Alignment

Deep Incomplete Multi-View Clustering with Cross-View Partia...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiaqi Jin Siwei Wang Zhibin Dong Xinwang Liu En Zhu School of Computer National University of Defense Technology Changsha China

The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following draw-backs: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility in representations; ii) Due to the absence of non-observed samples across multiple views, the obtained prototypes of clusters might be unaligned and biased, leading to incorrect fusion. To address the above issues, we propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction among views. Then, regarding of the shifted prototypes in IMVC, we further propose a prototype alignment module to achieve incomplete distribution calibration across views. Extensive experimental results showcase the effectiveness of our proposed modules, attaining noteworthy performance improvements when compared to existing IMVC competitors on benchmark datasets.

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Pose-disentangled Contrastive Learning for Self-supervised Facial Representation

Pose-disentangled Contrastive Learning for Self-supervised F...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuanyuan Liu Wenbin Wang Yibing Zhan Shaoze Feng Kejun Liu Zhe Chen School of Computer Science China University of Geosciences Wuhan China JD Explore Academy China The University of Sydney Australia

Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning (SSL) still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other's training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted linear evaluation on four challenging downstream facial understanding tasks, i.e., facial expression recognition, face recognition, AU detection and head pose estimation. Experimental results demonstrate that PCL significantly outperforms cuttingedge SSL methods. Our Code is available at https://***/DreamMr/PCL.

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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conference on computer vision and pattern recognition (CVPR)

作者： Xi Zhang Feifei Zhang Changsheng Xu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Tianjin University of Technology Peng Cheng Laboratory

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

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Boost vision Transformer with GPU-Friendly Sparsity and Quantization

Boost Vision Transformer with GPU-Friendly Sparsity and Quan...

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conference on computer vision and pattern recognition (CVPR)

作者： Chong Yu Tao Chen Zhongxue Gan Jiayuan Fan Academy for Engineering and Technology Fudan University NVIDIA Corporation School for Information Science and Technology Fudan University

The transformer extends its success from the language to the vision domain. Because of the stacked self-attention and cross-attention blocks, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models $\mathbf{6.4}-\mathbf{12.7}\times$ on model size and $\mathbf{30.3}-\mathbf{62} \times$ on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by $\mathbf{1.39}-\mathbf{1.79}\times$ and $\mathbf{3.22}-\mathbf{3.43}\times$ of latency and throughput on A100 GPU, and $\mathbf{1.57}-\mathbf{1.69}\times$ and $\mathbf{2.11}-\mathbf{2.51}\times$ improvement of latency and throughput on AGX Orin.

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TrojViT: Trojan Insertion in vision Transformers

TrojViT: Trojan Insertion in Vision Transformers

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conference on computer vision and pattern recognition (CVPR)

作者： Mengxin Zheng Qian Lou Lei Jiang Indiana University Bloomington University of Central Florida

vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform back-door attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Compared to CNNs capturing pixel-wise local features by convolutions, ViTs extract global context information through patches and attentions. Naively transplanting CNN-specific backdoor attacks to ViTs yields only a low clean data accuracy and a low attack success rate. In this paper, we propose a stealth and practical ViT-specific backdoor attack TrojViT. Rather than an area-wise trigger used by CNN-specific backdoor attacks, TrojViT generates a patch-wise trigger designed to build a Trojan composed of some vulnerable bits on the parameters of a ViT stored in DRAM memory through patch salience ranking and attention-target loss. TrojViT further uses parameter distillation to reduce the bit number of the Trojan. Once the attacker inserts the Trojan into the ViT model by flipping the vulnerable bits, the ViT model still produces normal inference accuracy with benign inputs. But when the attacker embeds a trigger into an input, the ViT model is forced to classify the input to a predefined target class. We show that flipping only few vulnerable bits identified by TrojViT on a ViT model using the well-known RowHammer can transform the model into a backdoored one. We perform extensive experiments of multiple datasets on various ViT models. TrojViT can classify 99.64% of test images to a target class by flipping 345 bits on a ViT for ImageNet.

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