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ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding

ULIP-2: Towards Scalable Multimodal Pre-training for 3D Unde...

引用

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

作者： Xue, Le Yu, Ning Zhang, Shu Panagopoulou, Artemis Li, Junnan Martin-Martin, Roberto Wu, Jiajun Xiong, Caiming Xu, Ran Niebles, Juan Carlos Savarese, Silvio Salesforce AI Res San Francisco CA 94105 USA Stanford Univ Stanford CA USA Univ Penn Philadelphia PA USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350353006

Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pre-training framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multi-modal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top-1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://***/salesforce/ULIP.

关键词： 3D vision Multimodal learning

来源：评论

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Learning Attention as Disentangler for Compositional Zero-shot Learning

Learning Attention as Disentangler for Compositional Zero-sh...

引用

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

作者： Hao, Shaozhe Han, Kai Wong, Kwan-Yee K. Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Compositional zero-shot learning (CZSL) aims at learning visual concepts (i.e., attributes and objects) from seen compositions and combining concept knowledge into unseen compositions. The key to CZSL is learning the disentanglement of the attribute-object composition. To this end, we propose to exploit cross-attentions as compositional disentanglers to learn disentangled concept embeddings. For example, if we want to recognize an unseen composition "yellow flower", we can learn the attribute concept "yellow" and object concept "flower" from different yellow objects and different flowers respectively. To further constrain the disentanglers to learn the concept of interest, we employ a regularization at the attention level. Specifically, we adapt the earth mover's distance (EMD) as a feature similarity metric in the cross-attention module. Moreover, benefiting from concept disentanglement, we improve the inference process and tune the prediction score by combining multiple concept probabilities. Comprehensive experiments on three CZSL benchmark datasets demonstrate that our method significantly outperforms previous works in both closed- and openworld settings, establishing a new state-of-the-art. Project page: https://***/ade-czsl/

关键词： detection recognition: Categorization retrieval

来源：评论

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CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search

CLIP2Protect: Protecting Facial Privacy using Text-Guided Ma...

引用

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

作者： Shamshad, Fahad Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate "naturalistic" images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user-defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://***/fahadshamshad/Clip2Protect.

关键词： accountability ethics in vision fairness privacy Transparency

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Learning to Generate Image Embeddings with User-level Differential Privacy

Learning to Generate Image Embeddings with User-level Differ...

引用

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

作者： Xu, Zheng Collins, Maxwell Wang, Yuxiao Panait, Liviu Oh, Sewoong Augenstein, Sean Liu, Ting Schroff, Florian McMahan, H. Brendan Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of epsilon < 2 while controlling the utility drop within 5%, when millions of users can participate in training

关键词： accountability ethics in vision fairness privacy Transparency

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Natural Language-Assisted Sign Language recognition

Natural Language-Assisted Sign Language Recognition

引用

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

作者： Zuo, Ronglai Wei, Fangyun Mak, Brian Hong Kong Univ Sci & Technol Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign languages, which limits the recognition capacity of vision neural networks. To mitigate the problem, we propose the Natural Language-Assisted Sign Language recognition (NLA-SLR) framework, which exploits semantic information contained in glosses (sign labels). First, for VISigns with similar semantic meanings, we propose language-aware label smoothing by generating soft labels for each training sign whose smoothing weights are computed from the normalized semantic similarities among the glosses to ease training. Second, for VISigns with distinct semantic meanings, we present an inter-modality mixup technique which blends vision and gloss features to further maximize the separability of different signs under the supervision of blended labels. Besides, we also introduce a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields. Empirically, our method achieves state-of-the-art performance on three widely-adopted benchmarks: MSASL, WLASL, and NMFs-CSL. Codes are available at https://***/FangyunWei/SLRT.

关键词： vision applications and systems

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Diversity-Measurable Anomaly Detection

Diversity-Measurable Anomaly Detection

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

作者： Liu, Wenrui Chang, Hong Ma, Bingpeng Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

关键词： detection recognition: Categorization retrieval

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REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

REVEAL: Retrieval-Augmented Visual-Language Pre-Training wit...

引用

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

作者： Hu, Ziniu Iscen, Ahmet Sun, Chen Wang, Zirui Chang, Kai-Wei Sun, Yizhou Schmid, Cordelia Ross, David A. Fathi, Alireza Univ Calif Los Angeles Los Angeles CA 90024 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. The project page of this work is ***.

关键词： and reasoning language vision

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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

Multimodality Helps Unimodality: Cross-Modal Few-Shot Learni...

引用

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

作者： Lin, Zhiqiu Yu, Samuel Kuang, Zhiyi Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better visual dog classifier by reading about dogs and listening to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification. Project site at link.

关键词： detection recognition: Categorization retrieval

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Enhanced Training of Query-Based Object Detection via Selective Query Recollection

Enhanced Training of Query-Based Object Detection via Select...

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

作者： Chen, Fangyi Zhang, Han Hu, Kai Huang, Yu-Kai Zhu, Chenchen Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI San Francisco CA USA

ISBN: (纸本)9798350301298

This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4 similar to 2.8 AP improvement. Code is available at https://***/Fangyi-Chen/SQR

关键词： detection recognition: Categorization retrieval

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MobileOne: An Improved One millisecond Mobile Backbone

MobileOne: An Improved One millisecond Mobile Backbone

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

作者： Vasu, Pavan Kumar Anasosalu Gabriel, James Zhu, Jeff Tuzel, Oncel Ranjan, Anurag Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350301298

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. Code and models are available at https://***/apple/ml-mobileone

关键词： Efficient and scalable vision

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