427 citations · 1.1k across the 82 of their papers we have counts for
82 papers
Representation Disparity-aware Distillation for 3D Object Detection
Yanjing Li, Sheng Xu, Mingbao Lin +3
In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher m…
Q-YOLO: Efficient Inference for Real-time Object Detection
Mingze Wang, Huixin Sun, Jun Shi +3
Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challen…
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler
Shaohui Lin, Wenxuan Huang, Jiao Xie +5
Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this pape…
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Yanjing Li, Sheng Xu, Xianbin Cao +4
Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by…
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
Yuguang Yang, Yiming Wang, Shupeng Geng +4
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval. However, on some domain-specific retrieval tasks, these models f…
Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map
Yuguang Yang, Runtang Guo, Sheng Wu +4
Interpretation of deep learning remains a very challenging problem. Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting objec…
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li, Sheng Xu, Mingbao Lin +4
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) th…
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
Runqi Wang, Hao Zheng, Xiaoyue Duan +5
Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feat…
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang +5
Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which…
Controllable Mind Visual Diffusion Model
Bohan Zeng, Shanglin Li, Xuhui Liu +6
Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Although diffusion mo…
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
Jie Guo, Qimeng Wang, Yan Gao +4
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investig…
Face Animation with an Attribute-Guided Diffusion Model
Bohan Zeng, Xuhui Liu, Sicheng Gao +4
Face animation has achieved much progress in computer vision. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion defo…
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu, Yanjing Li, Mingbao Lin +4
The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quanti…
Implicit Diffusion Models for Continuous Super-Resolution
Sicheng Gao, Xuhui Liu, Bohan Zeng +6
Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and mos…
Uncertainty-Aware Gradient Stabilization for Small Object Detection
Huixin Sun, Yanjing Li, Linlin Yang +2
Despite advances in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We reveal that conventional object locali…
Resilient Binary Neural Network
Sheng Xu, Yanjing Li, Teli Ma +5
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is…
Feature Calibration Network for Occluded Pedestrian Detection
Tianliang Zhang, Qixiang Ye, Baochang Zhang +3
Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the…
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection
Tianliang Zhang, Zhenjun Han, Huijuan Xu +2
Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Exi…
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning
Xiaoyue Duan, Guoliang Kang, Runqi Wang +4
Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to a…
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Yanjing Li, Sheng Xu, Baochang Zhang +3
The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems…
IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
Sheng Xu, Yanjing Li, Bohan Zeng +5
Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and stu…
FNeVR: Neural Volume Rendering for Face Animation
Bohan Zeng, Boyu Liu, Hong Li +5
Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models. However, it remains a critical challenge to g…
Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu, Yanjing Li, Tiancheng Wang +6
Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an esse…
Anti-Retroactive Interference for Lifelong Learning
Runqi Wang, Yuxiang Bao, Baochang Zhang +3
Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive scie…