arXiv subjects

Shuqiang Jiang

Publications and source records attributed to Shuqiang Jiang.

42 records · Page 3Linked to original sources

arXiv2019

Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition

In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.

cs.CV↗

arXiv2018

Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction

In this work, we focus on visual venue category prediction, which can facilitate various applications for location-based service and personalization. Considering that the complementarity of different media platforms, it is reasonable to leverage venue-relevant media data from different platforms to boost the prediction performance. Intuitively, recognizing one venue category involves multiple semantic cues, especially objects and scenes, and thus they should contribute together to venue category prediction. In addition, these venues can be organized in a natural hierarchical structure, which provides prior knowledge to guide venue category estimation. Taking these aspects into account, we propose a Hierarchy-dependent Cross-platform Multi-view Feature Learning (HCM-FL) framework for venue category prediction from videos by leveraging images from other platforms. HCM-FL includes two major components, namely Cross-Platform Transfer Deep Learning (CPTDL) and Multi-View Feature Learning with the Hierarchical Venue Structure (MVFL-HVS). CPTDL is capable of reinforcing the learned deep network from videos using images from other platforms. Specifically, CPTDL first trained a deep network using videos. These images from other platforms are filtered by the learnt network and these selected images are then fed into this learnt network to enhance it. Two kinds of pre-trained networks on the ImageNet and Places dataset are employed. MVFL-HVS is then developed to enable multi-view feature fusion. It is capable of embedding the hierarchical structure ontology to support more discriminative joint feature learning. We conduct the experiment on videos from Vine and images from Foursqure. These experimental results demonstrate the advantage of our proposed framework.

cs.MM↗

arXiv2018

Learning Effective RGB-D Representations for Scene Recognition

Deep convolutional networks (CNN) can achieve impressive results on RGB scene recognition thanks to large datasets such as Places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5m to 5.5m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scene. Focusing on this scenario, we introduce the ISIA RGB-D video dataset to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks (RNNs) that are trained in three steps with increasingly complex data to learn effective features (i.e. patches, frames and sequences). Our approach obtains state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition.

cs.CV↗

arXiv2018

Food recognition and recipe analysis: integrating visual content, context and external knowledge

The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation, and the restaurant context as emerging directions.

cs.CV↗

arXiv2018

Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.

cs.CV↗

arXiv2018

Scene recognition with CNNs: objects, scales and dataset bias

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN, showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the state-of-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).

cs.CV↗