Jian Sun

Bio: Jian Sun is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Object detection & Computer science. The author has an hindex of 109, co-authored 360 publications receiving 239387 citations. Previous affiliations of Jian Sun include French Institute for Research in Computer Science and Automation & Tsinghua University.

Visual cue refining for user inquiry results

Abstract: The invention provides a method facilitating refining of inquiry results via visual cues, and a computer storage medium comprising an instruction capable of being executed by a computer facilitating refining of inquiry results via visual cues. In response to instructions of user inquiry, inquiry results are determined. According to categories of the inquiry results with common similar characteristics, a group or many groups of inquiry results can be generated from the inquiry results. The visual cues are associated with each group of the inquiry result groups. The visual cues associated with the inquiry result groups are presented to users. The inquiry results associated selected visual cues can be presented to users. According to the selected visual cues, refined user inquiry can be generated.

Scale-Aware Automatic Augmentations for Object Detection With Dynamic Training

Abstract: Data augmentation is a critical technique in object detection, especially the augmentations targeting at scale invariance training (scale-aware augmentation). However, there has been little systematic investigation of how to design scale-aware data augmentation for object detection. We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and instance-level augmentations are designed for maintaining scale robust feature learning. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilitate efficient augmentation policy search. In experiments, Scale-aware AutoAug yields significant and consistent improvement on various object detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with strong multi-scale training baselines. Our searched augmentation policies are generalized well to other datasets and instance-level tasks beyond object detection, e.g., instance segmentation. The search cost is much less than previous automated augmentation approaches for object detection, i.e., 8 GPUs across 2.5 days versus. 800 TPU-days. In addition, meaningful patterns can be summarized from our searched policies, which intuitively provide valuable knowledge for hand-crafted data augmentation design. Based on the searched scale-aware augmentation policies, we further introduce a dynamic training paradigm to adaptively determine specific augmentation policy usage during training. The dynamic paradigm consists of an heuristic manner for image-level augmentations and a differentiable copy-paste-based method for instance-level augmentations. The dynamic paradigm achieves further performance improvements to Scale-aware AutoAug without any additional burden on the long tailed LVIS benchmarks. We also demonstrate its ability to prevent over-fitting for large models, e.g., the Swin Transformer large model. Code and models are available at https://github.com/dvlab-research/SA-AutoAug.

LGD: Label-guided Self-distillation for Object Detection

Abstract: In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). Previous studies rely on a strong pretrained teacher to provide instructive knowledge for distillation. However, this could be unavailable in real-world scenarios. Instead, we generate an instructive knowledge by inter-and-intra relation modeling among objects, requiring only student representations and regular labels. In detail, our framework involves sparse label-appearance encoding, inter-object relation adaptation and intra-object knowledge mapping to obtain the instructive knowledge. Modules in LGD are trained end-to-end with student detector and are discarded in inference. Empirically, LGD obtains decent results on various detectors, datasets, and extensive task like instance segmentation. For example in MS-COCO dataset, LGD improves RetinaNet with ResNet-50 under 2x single-scale training from 36.2% to 39.0% mAP (+ 2.8%). For much stronger detectors like FCOS with ResNeXt-101 DCN v2 under 2x multi-scale training (46.1%), LGD achieves 47.9% (+ 1.8%). For pedestrian detection in CrowdHuman dataset, LGD boosts mMR by 2.3% for Faster R-CNN with ResNet-50. Compared with a classical teacher-based method FGFI, LGD not only performs better without requiring pretrained teacher but also with 51% lower training cost beyond inherent student learning.

Learning Regularized, Query-Dependent Bilinear Similarities for Large Scale Image Retrieval

Abstract: An effective way to improve the quality of image retrieval is by employing a query-dependent similarity measure. However, implementing this in a large scale system is non-trivial because we want neither hurting the efficiency nor relying on too many training samples. In this paper, we introduce a query-dependent bilinear similarity measure to address the first issue. Based on our bilinear similarity model, query adaptation can be achieved by simply applying any existing efficient indexing/retrieval method to a transformed version (surrogate) of a query. To address the issue of limited training samples, we further propose a novel angular regularization constraint for learning the similarity measure. The learning is formulated as a Quadratic Programming (QP) problem and can be solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-million web-image dataset validate that our proposed method can consistently bring improvements and the whole solution is practical in large scale applications.

Harmonic Extension

Abstract: In this paper, we consider the harmonic extension problem, which is widely used in many applications of machine learning We find that the transitional method of graph Laplacian fails to produce a good approximation of the classical harmonic function To tackle this problem, we propose a new method called the point integral method (PIM) We consider the harmonic extension problem from the point of view of solving PDEs on manifolds The basic idea of the PIM method is to approximate the harmonicity using an integral equation, which is easy to be discretized from points Based on the integral equation, we explain the reason why the transitional graph Laplacian may fail to approximate the harmonicity in the classical sense and propose a different approach which we call the volume constraint method (VCM) Theoretically, both the PIM and the VCM computes a harmonic function with convergence guarantees, and practically, they are both simple, which amount to solve a linear system One important application of the harmonic extension in machine learning is semi-supervised learning We run a popular semi-supervised learning algorithm by Zhu et al over a couple of well-known datasets and compare the performance of the aforementioned approaches Our experiments show the PIM performs the best

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

U-Net: Convolutional Networks for Biomedical Image Segmentation

Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.