Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations using large-scale image-text pairs. It shows impressive performance on downstream tasks by zero-shot knowledge transfer. To further enhance CLIP's adaption capability, existing methods proposed to fine-tune additional learnable modules, which significantly improves the few-shot performance but introduces extra training time and computational resources. In this paper, we propose a training-free adaption method for CLIP to conduct few-shot classification, termed as Tip-Adapter, which not only inherits the training-free advantage of zero-shot CLIP but also performs comparably to those training-required approaches. Tip-Adapter constructs the adapter via a key-value cache model from the few-shot training set, and updates the prior knowledge encoded in CLIP by feature retrieval. On top of that, the performance of Tip-Adapter can be further boosted to be state-of-the-art on ImageNet by fine-tuning the cache model for 10$\times$ fewer epochs than existing methods, which is both effective and efficient. We conduct extensive experiments of few-shot classification on 11 datasets to demonstrate the superiority of our proposed methods. Code is released at https://github.com/gaopengcuhk/Tip-Adapter.

Can I use this publicly available dataset to build commercial AI software? Most likely not

Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets (particularly for commercial purposes) is governed by dataset licenses. These dataset licenses outline the rights one is entitled to on a given dataset and the obligations that one must fulfil to enjoy such rights without any license compliance violations. However, unlike standardized Open Source Software (OSS) licenses, existing dataset licenses are defined in an ad-hoc manner and do not clearly outline the rights and obligations associated with their usage. This makes checking for potential license compliance violations difficult. Further, a public dataset may be hosted in multiple locations and created from multiple data sources each of which may have different licenses. Hence, existing approaches on checking OSS license compliance cannot be used. In this paper, we propose a new approach to assess the potential license compliance violations if a given publicly available dataset were to be used for building commercial AI software. We conduct trials of our approach on two product groups within Huawei on 6 commonly used publicly available datasets. Our results show that there are risks of license violations on 5 of these 6 studied datasets if they were used for commercial purposes. Consequently, we provide recommendations for AI engineers on how to better assess publicly available datasets for license compliance violations.

CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach

We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system. We describe how the sample spatial covariance matrix computed from the CSI can be used as the input to a learning algorithm which attempts to relate it to user location. We discuss several learning approaches, and analyze in depth the application of extreme learning machines, for which theoretical approximate performance benchmarks are available, to the localization problem. We validate the proposed approach using experimental data collected on a Huawei 5G testbed, provide some performance and robustness benchmarks, and discuss practical issues related to the deployment of such a technique in 5G networks.