133 citations · 724 across the 36 of their papers we have counts for
38 papers
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Zhiwei Chen, Changan Wang, Yabiao Wang +6
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result i…
PointCLIP: Point Cloud Understanding by CLIP
Renrui Zhang, Ziyu Guo, Wei Zhang +6
Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match i…
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Mohan Zhou, Yalong Bai, Wei Zhang +3
We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensabl…
Directional Self-supervised Learning for Heavy Image Augmentations
Yalong Bai, Yifan Yang, Wei Zhang +1
Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning. In this paper, we prop…
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
Renrui Zhang, Rongyao Fang, Wei Zhang +5
Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations by using large-scale contrastive image-text pairs. It shows…
ViDA-MAN: Visual Dialog with Digital Humans
Tong Shen, Jiawei Zuo, Fan Shi +7
We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries. Compared to traditional text o…
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
Pan Lu, Liang Qiu, Jiaqi Chen +6
Current visual question answering (VQA) tasks mainly consider answering human-annotated questions for natural images. However, aside from natural images, abstract diagrams with sem…
Video Temporal Relationship Mining for Data-Efficient Person Re-identification
Siyu Chen, Dengjie Li, Lishuai Gao +3
This paper is a technical report to our submission to the ICCV 2021 VIPriors Re-identification Challenge. In order to make full use of the visual inductive priors of the data, we t…
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
Tong Sha, Wei Zhang, Tong Shen +2
Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production. With…
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation
Lewei Yao, Renjie Pi, Hang Xu +3
In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous stud…
Greedy Network Enlarging
Chuanjian Liu, Kai Han, An Xiao +4
Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracy, such as Effici…
Augmentation Pathways Network for Visual Recognition
Yalong Bai, Mohan Zhou, Wei Zhang +2
Data augmentation is practically helpful for visual recognition, especially at the time of data scarcity. However, such success is only limited to quite a few light augmentations (…
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Jianhua Han, Xiwen Liang, Hang Xu +8
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-super…
One Million Scenes for Autonomous Driving: ONCE Dataset
Jiageng Mao, Minzhe Niu, Chenhan Jiang +10
Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On th…
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Lewei Yao, Renjie Pi, Hang Xu +3
We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead o…
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David S. Kung, Geert Janssen +14
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…
Points as Queries: Weakly Semi-supervised Object Detection by Points
Liangyu Chen, Tong Yang, Xiangyu Zhang +2
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annota…
Exploiting Relationship for Complex-scene Image Generation
Tianyu Hua, Hongdong Zheng, Yalong Bai +3
The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generati…
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Yunhe Wang, Mingqiang Huang, Kai Han +4
Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally inten…
BinaryBERT: Pushing the Limit of BERT Quantization
Haoli Bai, Wei Zhang, Lu Hou +6
The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In thi…
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Bochao Wang, Hang Xu, Jiajin Zhang +21
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated Auto…
Kernel Based Progressive Distillation for Adder Neural Networks
Yixing Xu, Chang Xu, Xinghao Chen +3
Adder Neural Networks (ANNs) which only contain additions bring us a new way of developing deep neural networks with low energy consumption. Unfortunately, there is an accuracy dro…
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Wei Zhang, Lu Hou, Yichun Yin +4
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks.However, these models are both computation and memory…
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
Kai Han, Yunhe Wang, Qiulin Zhang +3
To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth…