1 code implementation • 30 Nov 2023 • Rongyao Fang, Shilin Yan, Zhaoyang Huang, Jingqiu Zhou, Hao Tian, Jifeng Dai, Hongsheng Li
In this work, we introduce InstructSeq, an instruction-conditioned multi-modal modeling framework that unifies diverse vision tasks through flexible natural language control and handling of both visual and textual data.
1 code implementation • 23 Nov 2023 • Yu Yi, Xue Yang, Qingyun Li, Feipeng Da, Junchi Yan, Jifeng Dai, Yu Qiao
To our best knowledge, Point2RBox is the first end-to-end solution for point-supervised OOD.
1 code implementation • 26 Oct 2023 • Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao, Erfei Cui, Zhiheng Li, Xizhou Zhu, Lewei Lu, Qifeng Chen, Yu Qiao, Jifeng Dai, Wenhai Wang
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
1 code implementation • 11 Oct 2023 • Zeqiang Lai, Xizhou Zhu, Jifeng Dai, Yu Qiao, Wenhai Wang
The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models.
1 code implementation • 3 Aug 2023 • Weiyun Wang, Min Shi, Qingyun Li, Wenhai Wang, Zhenhang Huang, Linjie Xing, Zhe Chen, Hao Li, Xizhou Zhu, Zhiguo Cao, Yushi Chen, Tong Lu, Jifeng Dai, Yu Qiao
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world.
no code implementations • 8 Jun 2023 • Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Yijin Li, Hongwei Qin, Jifeng Dai, Xiaogang Wang, Hongsheng Li
This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation.
no code implementations • 8 Jun 2023 • Changyao Tian, Chenxin Tao, Jifeng Dai, Hao Li, Ziheng Li, Lewei Lu, Xiaogang Wang, Hongsheng Li, Gao Huang, Xizhou Zhu
In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels.
no code implementations • 2 Jun 2023 • Zeqiang Lai, Yuchen Duan, Jifeng Dai, Ziheng Li, Ying Fu, Hongsheng Li, Yu Qiao, Wenhai Wang
In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model.
1 code implementation • 25 May 2023 • Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, Yu Qiao, Zhaoxiang Zhang, Jifeng Dai
These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions.
no code implementations • NeurIPS 2023 • Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, Ping Luo
In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities.
2 code implementations • NeurIPS 2023 • Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie zhou, Yu Qiao, Jifeng Dai
We hope this model can set a new baseline for generalist vision and language models.
2 code implementations • 9 May 2023 • Zhaoyang Liu, Yinan He, Wenhai Wang, Weiyun Wang, Yi Wang, Shoufa Chen, Qinglong Zhang, Zeqiang Lai, Yang Yang, Qingyun Li, Jiashuo Yu, Kunchang Li, Zhe Chen, Xue Yang, Xizhou Zhu, Yali Wang, LiMin Wang, Ping Luo, Jifeng Dai, Yu Qiao
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
1 code implementation • CVPR 2023 • Jiaqi Xu, Xiaowei Hu, Lei Zhu, Qi Dou, Jifeng Dai, Yu Qiao, Pheng-Ann Heng
Video dehazing aims to recover haze-free frames with high visibility and contrast.
1 code implementation • ICCV 2023 • Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li
We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner.
1 code implementation • CVPR 2023 • Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li
FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance.
1 code implementation • 2 Mar 2023 • Rongyao Fang, Peng Gao, Aojun Zhou, Yingjie Cai, Si Liu, Jifeng Dai, Hongsheng Li
The first method is One-to-many Matching via Data Augmentation (denoted as DataAug-DETR).
no code implementations • CVPR 2023 • Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, Xiaowei Hu
Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features.
1 code implementation • CVPR 2023 • Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, Hongyang Li
Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.
2 code implementations • CVPR 2023 • Chenyu Yang, Yuntao Chen, Hao Tian, Chenxin Tao, Xizhou Zhu, Zhaoxiang Zhang, Gao Huang, Hongyang Li, Yu Qiao, Lewei Lu, Jie zhou, Jifeng Dai
The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
Ranked #4 on
3D Object Detection
on Rope3D
2 code implementations • CVPR 2023 • Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu, Hongsheng Li, Chun Yuan, Xiaohua Wang, Yu Qiao, Xiaogang Wang, Wenhai Wang, Jifeng Dai
In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance.
1 code implementation • CVPR 2023 • Weijie Su, Xizhou Zhu, Chenxin Tao, Lewei Lu, Bin Li, Gao Huang, Yu Qiao, Xiaogang Wang, Jie zhou, Jifeng Dai
It has been proved that combining multiple pre-training strategies and data from various modalities/sources can greatly boost the training of large-scale models.
Ranked #2 on
Object Detection
on LVIS v1.0 minival
(using extra training data)
1 code implementation • 10 Nov 2022 • Jifeng Dai, Min Shi, Weiyun Wang, Sitong Wu, Linjie Xing, Wenhai Wang, Xizhou Zhu, Lewei Lu, Jie zhou, Xiaogang Wang, Yu Qiao, Xiaowei Hu
Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, which makes it hard to identify the real gain from the key feature transformation operators.
2 code implementations • CVPR 2023 • Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, Xiaogang Wang, Yu Qiao
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state.
Ranked #1 on
Instance Segmentation
on COCO test-dev
(APS metric, using extra
training data)
2 code implementations • 12 Sep 2022 • Hongyang Li, Chonghao Sima, Jifeng Dai, Wenhai Wang, Lewei Lu, Huijie Wang, Jia Zeng, Zhiqi Li, Jiazhi Yang, Hanming Deng, Hao Tian, Enze Xie, Jiangwei Xie, Li Chen, Tianyu Li, Yang Li, Yulu Gao, Xiaosong Jia, Si Liu, Jianping Shi, Dahua Lin, Yu Qiao
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
2 code implementations • 6 Aug 2022 • Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, Hongsheng Li
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos.
Ranked #23 on
Action Classification
on Kinetics-400
(using extra training data)
1 code implementation • 19 Jul 2022 • Renrui Zhang, Zhang Wei, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, Hongsheng Li
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.
1 code implementation • 9 Jun 2022 • Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai
To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models.
2 code implementations • CVPR 2023 • Chenxin Tao, Xizhou Zhu, Weijie Su, Gao Huang, Bin Li, Jie zhou, Yu Qiao, Xiaogang Wang, Jifeng Dai
Driven by these analysis, we propose Siamese Image Modeling (SiameseIM), which predicts the dense representations of an augmented view, based on another masked view from the same image but with different augmentations.
1 code implementation • 17 May 2022 • Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT).
Ranked #4 on
Semantic Segmentation
on PASCAL Context
4 code implementations • 8 May 2022 • Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao
Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer architectures can further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and semantic segmentation.
2 code implementations • 31 Mar 2022 • Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, Jifeng Dai
In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries.
Ranked #3 on
Robust Camera Only 3D Object Detection
on nuScenes-C
1 code implementation • 30 Mar 2022 • Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, Hongsheng Li
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
1 code implementation • NeurIPS 2021 • Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong liu, Jifeng Dai
In this paper, we propose Parameterized AP Loss, where parameterized functions are introduced to substitute the non-differentiable components in the AP calculation.
1 code implementation • CVPR 2022 • Chenxin Tao, Honghui Wang, Xizhou Zhu, Jiahua Dong, Shiji Song, Gao Huang, Jifeng Dai
These methods appear to be quite different in the designed loss functions from various motivations.
1 code implementation • CVPR 2022 • Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Xiaogang Wang, Hongsheng Li, Xiaohua Wang, Jifeng Dai
The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.
1 code implementation • 26 Nov 2021 • Changyao Tian, Wenhai Wang, Xizhou Zhu, Jifeng Dai, Yu Qiao
Deep learning-based models encounter challenges when processing long-tailed data in the real world.
Ranked #2 on
Long-tail Learning
on iNaturalist 2018
(using extra training data)
1 code implementation • 6 Nov 2021 • Renrui Zhang, Rongyao Fang, Wei zhang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, Hongsheng Li
To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification.
1 code implementation • ICCV 2021 • Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, Hongsheng Li
On the contrary, the soft composition operates by stitching different patches into a whole feature map where pixels in overlapping regions are summed up.
Ranked #4 on
Video Inpainting
on DAVIS
1 code implementation • ICCV 2021 • Zhuoming Liu, Hao Ding, Huaping Zhong, Weijia Li, Jifeng Dai, Conghui He
To obtain the Influence of the unlabeled sample in the active learning scenario, we design the Untrained Unlabeled sample Influence Calculation(UUIC) to estimate the unlabeled sample's expected gradient with which we calculate its Influence.
1 code implementation • ICCV 2021 • Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, Hongsheng Li
However, DETR suffers from its slow convergence.
1 code implementation • 2 Jul 2021 • Haiyang Wang, Wenguan Wang, Xizhou Zhu, Jifeng Dai, LiWei Wang
As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques.
no code implementations • 4 Jun 2021 • Peng Gao, Shijie Geng, Yu Qiao, Xiaogang Wang, Jifeng Dai, Hongsheng Li
In this paper, we propose a novel Scalable Transformers, which naturally contains sub-Transformers of different scales and have shared parameters.
1 code implementation • 14 Apr 2021 • Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, Hongsheng Li
Seamless combination of these two novel designs forms a better spatial-temporal attention scheme and our proposed model achieves better performance than state-of-the-art video inpainting approaches with significant boosted efficiency.
no code implementations • CVPR 2022 • Hao Li, Tianwen Fu, Jifeng Dai, Hongsheng Li, Gao Huang, Xizhou Zhu
However, the automatic design of loss functions for generic tasks with various evaluation metrics remains under-investigated.
5 code implementations • ICCV 2021 • Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, Luc van Gool
Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.
2 code implementations • 19 Jan 2021 • Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, Hongsheng Li
The recently proposed Detection Transformer (DETR) model successfully applies Transformer to objects detection and achieves comparable performance with two-stage object detection frameworks, such as Faster-RCNN.
no code implementations • CVPR 2021 • Hao Tian, Yuntao Chen, Jifeng Dai, Zhaoxiang Zhang, Xizhou Zhu
We further identify another major issue, seldom noticed by the community, that the long-tailed and open-ended (sub-)category distribution should be accommodated.
1 code implementation • ICLR 2021 • Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai
In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric.
17 code implementations • ICLR 2021 • Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance.
Ranked #34 on
Object Detection
on COCO-O
no code implementations • 3 Sep 2020 • Jingru Tan, Gang Zhang, Hanming Deng, Changbao Wang, Lewei Lu, Quanquan Li, Jifeng Dai
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020.
Ranked #1 on
Instance Segmentation
on LVIS v1.0 test-dev
2 code implementations • ECCV 2020 • Guolei Sun, Wenguan Wang, Jifeng Dai, Luc van Gool
Moreover, our approach ranked 1st place in the Weakly-Supervised Semantic Segmentation Track of CVPR2020 Learning from Imperfect Data Challenge.
2 code implementations • CVPR 2020 • Le Yang, Yizeng Han, Xi Chen, Shiji Song, Jifeng Dai, Gao Huang
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks.
1 code implementation • CVPR 2020 • Wenguan Wang, Hailong Zhu, Jifeng Dai, Yanwei Pang, Jianbing Shen, Ling Shao
As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this task.
2 code implementations • ICLR 2020 • Hang Gao, Xizhou Zhu, Steve Lin, Jifeng Dai
This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field.
Ranked #186 on
Object Detection
on COCO test-dev
3 code implementations • ICLR 2020 • Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai
We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short).
Ranked #1 on
Visual Question Answering (VQA)
on VCR (Q-A) dev
143 code implementations • 17 Jun 2019 • Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin
In this paper, we introduce the various features of this toolbox.
1 code implementation • ICCV 2019 • Xizhou Zhu, Dazhi Cheng, Zheng Zhang, Stephen Lin, Jifeng Dai
Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance.
23 code implementations • CVPR 2019 • Xizhou Zhu, Han Hu, Stephen Lin, Jifeng Dai
The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.
Ranked #130 on
Object Detection
on COCO test-dev
no code implementations • 27 Nov 2018 • Zheng Zhang, Dazhi Cheng, Xizhou Zhu, Stephen Lin, Jifeng Dai
Accurate detection and tracking of objects is vital for effective video understanding.
Ranked #16 on
Video Object Detection
on ImageNet VID
3 code implementations • 16 Apr 2018 • Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan
In this paper, we present a light weight network architecture for video object detection on mobiles.
no code implementations • ECCV 2018 • Jiayuan Gu, Han Hu, Li-Wei Wang, Yichen Wei, Jifeng Dai
While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods.
6 code implementations • CVPR 2018 • Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
no code implementations • CVPR 2018 • Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei
There has been significant progresses for image object detection in recent years.
2 code implementations • ICCV 2017 • Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #22 on
Video Object Detection
on ImageNet VID
38 code implementations • ICCV 2017 • Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.
Ranked #3 on
Vessel Detection
on Vessel detection Dateset
3 code implementations • CVPR 2017 • Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei
It inherits all the merits of FCNs for semantic segmentation and instance mask proposal.
Ranked #95 on
Instance Segmentation
on COCO test-dev
3 code implementations • CVPR 2017 • Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei
Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable.
Ranked #9 on
Video Semantic Segmentation
on Cityscapes val
46 code implementations • NeurIPS 2016 • Jifeng Dai, Yi Li, Kaiming He, Jian Sun
In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
Ranked #4 on
Real-Time Object Detection
on PASCAL VOC 2007
no code implementations • CVPR 2016 • Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, Jian Sun
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure.
no code implementations • 29 Mar 2016 • Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, Jian Sun
In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances.
2 code implementations • CVPR 2016 • Jifeng Dai, Kaiming He, Jian Sun
We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
Ranked #3 on
Multi-Human Parsing
on PASCAL-Part
no code implementations • ICCV 2015 • Jifeng Dai, Kaiming He, Jian Sun
Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with human-annotated, pixel-level segmentation masks.
Ranked #46 on
Semantic Segmentation
on PASCAL VOC 2012 test
no code implementations • 19 Dec 2014 • Jifeng Dai, Yang Lu, Ying-Nian Wu
(2) We propose a generative gradient for pre-training CNNs by a non-parametric importance sampling scheme, which is fundamentally different from the commonly used discriminative gradient, and yet has the same computational architecture and cost as the latter.
1 code implementation • CVPR 2015 • Jifeng Dai, Kaiming He, Jian Sun
The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions.
Ranked #61 on
Semantic Segmentation
on PASCAL Context
no code implementations • CVPR 2014 • Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu
Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary.