Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary – the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a consistent block convolution that encourages the network to learn the category-consistent features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and demonstrate the proposed method consistently outperforms existing alternatives or delivers competitive results. Codes are available at https://github.com/PRIS-CV/PMG-V2 .

Metric-based methods achieve promising performance on few-shot classification by learning clusters on support samples and generating shared decision boundaries for query samples. However, existing methods ignore the inaccurate class center approximation introduced by the limited number of support samples, which consequently leads to biased inference. Therefore, in this paper, we propose to reduce the approximation error by class center calibration. Specifically, we introduce the so-called Pair-wise Similarity Module (PSM) to generate calibrated class centers adapted to the query sample by capturing the semantic correlations between the support and the query samples, as well as enhancing the discriminative regions on support representation. It is worth noting that the proposed PSM is a simple plug-and-play module and can be inserted into most metric-based few-shot learning models. Through extensive experiments in metric-based models, we demonstrate that the module significantly improves the performance of conventional few-shot classification methods on four few-shot image classification benchmark datasets. Codes are available at: https://github.com/PRIS-CV/Pair-wise-Similarity-module .

Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, we discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. We experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, we propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS .

As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of “Whip-poor-will” or “Mallard” probably does not make much sense. This however commonly accepted in the literature, underlines a fundamental question interfacing AI and human – what constitutes transferable knowledge for human to learn from AI? This paper sets out to answer this very question using FGVC as a test bed. Specifically, we envisage a scenario where a trained FGVC model (the AI expert) functions as a knowledge provider in enabling average people (you and me) to become better domain experts ourselves, i.e., those capable in distinguishing between “Whip-poor-will” and “Mallard”. Fig. 1 lays out our approach in answering this question. Assuming an AI expert trained using expert human labels, we ask (i) what is the best transferable knowledge we can extract from AI, and (ii) what is the most practical means to measure the gains in expertise given that knowledge? On the former, we propose to represent knowledge as highly discriminative visual regions that are expert-exclusive. For that, we devise a multi-stage learning framework, which starts with modelling visual attention of domain experts and novices separately, before discriminatively distilling their differences to acquire those exclusive to experts. For the latter, we simulate the evaluation process as a book guide to best accommodate the learning practice of that is accustomed to humans. A comprehensive human study of 15,000 trials shows our method is able to consistently improve people of divergent bird expertise to recognise once unrecognisable birds. To counter the lack of reproducibility of perceptual studies, and in turn to make a sustainable direction out of our “AI for Human” effort, we further propose a quantitative metric, namely Transferable Effective Model Attention (TEMI). TEMI acts as a crude but benchmarkable metric to replace large-scale human studies, and therefore allows future efforts in this direction to be comparable to ours. We attest to the integrity of TEMI by (i) empirically showing a strong correlation between TEMI scores and raw human study data, and (ii) its expected behaviour holds for a large body of attention models. Last but not least, our approach also leads to improved FGVC performance in the conventional benchmarking sense, when the extracted knowledge defined is utilised as means to achieve discriminative localisation. Codes and all details on the human study are available at: https://github.com/PRIS-CV/Making-a-Bird-AI-Expert-Work-for-You-and-Me .