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PromptKD: Unsupervised Prompt Distillation for vision-Language Models

PromptKD: Unsupervised Prompt Distillation for Vision-Langua...

引用

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

作者： Li, Zheng Li, Xiang Fu, Xinyi Zhang, Xin Wang, Weiqiang Chen, Shuo Yang, Jian Nankai Univ Coll Comp Sci PCA Lab VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China Ant Grp Tiansuan Lab Hangzhou Peoples R China RIKEN Wako Saitama Japan

ISBN: (纸本)9798350353006

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pre-training, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://***/zhengli97/PromptKD.

关键词： knowledge distillation prompt learning vision-language models zero-shot learning

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Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection

Optimal Proposal Learning for Deployable End-to-End Pedestri...

引用

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

作者： Song, Xiaolin Chen, Binghui Li, Pengyu He, Jun-Yan Wang, Biao Geng, Yifeng Xie, Xuansong Zhang, Honggang Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods.

关键词： detection recognition: Categorization retrieval

来源：评论

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iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

引用

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

作者： Wei, Yixuan Cao, Yue Zhang, Zheng Peng, Houwen Yao, Zhuliang Xie, Zhenda Hue, Han Guo, Baining Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and meanwhile surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https: //***/weiyx16/iCLIP.

关键词： language reasoning vision

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Benchmarking Self-Supervised Learning on Diverse Pathology D...

引用

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

作者： Kang, Mingu Song, Heon Park, Seonwook Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning (SSL) has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements. We release the pre-trained model weights(1).

关键词： cell microscopy Medical and biological vision

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Hairy Ground Truth Enhancement for Semantic Segmentation

Hairy Ground Truth Enhancement for Semantic Segmentation

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

作者： Fischer, Sophie Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England

ISBN: (纸本)9798350365474

Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks;yet many models consider two false positive (or false negative) pixel predictions as 'equally wrong' regardless of the individual pixels' relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous group. We propose the Hairy transform, a novel method to enhance ground truths using 3D 'hairs' to represent each pixel's position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset, as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one-off pre-processing step, and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct 'standard' masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.

关键词： computer vision Ground Truth Enhancement Machine Learning Medical Imaging

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

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

作者： Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada

ISBN: (纸本)9798350365474

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset's distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/ himsR/DataCVChallenge-2024/tree/main

关键词： computer vision Data Search deep learning domain Transfer

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CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Div...

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

作者： Sanghi, Aditya Fu, Rao Liu, Vivian Willis, Karl D. D. Shayani, Hooman Khasahmadi, Amir H. Sridhar, Srinath Ritchie, Daniel Autodesk Res San Francisco CA 94105 USA Brown Univ Providence RI USA Columbia Univ New York NY USA

ISBN: (纸本)9798350301298

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor achieves this in a multi-resolution approach that first generates in a low-dimensional latent space and then upscales to a higher resolution for improved shape fidelity. For improved shape diversity, we use a discrete latent space which is modeled using a transformer conditioned on CLIP's image-text embedding space. We also present a novel variant of classifier-free guidance, which improves the accuracy-diversity trade-off. Finally, we perform extensive experiments demonstrating that CLIP-Sculptor outperforms state-of-the-art baselines.

关键词： vision + graphics

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Joint Token Pruning and Squeezing Towards More Aggressive Compression of vision Transformers

Joint Token Pruning and Squeezing Towards More Aggressive Co...

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

作者： Wei, Siyuan Ye, Tianzhu Zhang, Shen Tang, Yao Liang, Jiajun MEGVII Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Although vision transformers (ViTs) have shown promising results in various computer vision tasks recently, their high computational cost limits their practical applications. Previous approaches that prune redundant tokens have demonstrated a good trade-off between performance and computation costs. Nevertheless, errors caused by pruning strategies can lead to significant information loss. Our quantitative experiments reveal that the impact of pruned tokens on performance should be noticeable. To address this issue, we propose a novel joint Token Pruning & Squeezing module (TPS) for compressing vision transformers with higher efficiency. Firstly, TPS adopts pruning to get the reserved and pruned subsets. Secondly, TPS squeezes the information of pruned tokens into partial reserved tokens via the unidirectional nearest-neighbor matching and similarity-based fusing steps. Compared to state-of-the-art methods, our approach outperforms them under all token pruning intensities. Especially while shrinking DeiT-tiny&small computational budgets to 35%, it improves the accuracy by 1%-6% compared with baselines on ImageNet classification. The proposed method can accelerate the throughput of DeiT-small beyond DeiT-tiny, while its accuracy surpasses DeiT-tiny by 4.78%. Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy. Code is available at https://***/megvii-research/TPS-cvpr2023.

关键词： Deep learning architectures and techniques

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Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration

Towards Building Self-Aware Object Detectors via Reliable Un...

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

作者： Oksuz, Kemal Joy, Tom Dokania, Puneet K. Five AI Ltd Cambridge England

ISBN: (纸本)9798350301298

The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift;obtain reliable uncertainty estimates for the entire scene;and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at: https://***/fiveai/saod.

关键词： detection recognition: Categorization retrieval

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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

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

作者： Ahousamra, Shahira Gupta, Rajarsi Kurc, Tahsin Samaras, Dimitris Saltz, Joel Chen, Chao SUNY Stony Brook Dept Comp Sci Stony Brook NY 11790 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY USA

ISBN: (纸本)9798350301298

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

关键词： cell microscopy Medical and biological vision

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