974 Result(s)
within
**li Zhang
Image Processing and Computer Vision
Information Systems Applications (incl. Internet)
Pattern Recognition
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Chapter and Conference Paper
CMC_v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In our approach, we employ the win...
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Chapter and Conference Paper
Boosting COVID-19 Severity Detection with Infection-Aware Contrastive Mixup Classification
This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach,...
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Chapter and Conference Paper
BiTAT: Neural Network Binarization with Task-Dependent Aggregated Transformation
Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving ...
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Chapter and Conference Paper
BadDet: Backdoor Attacks on Object Detection
Backdoor attack is a severe security threat which injects a backdoor trigger into a small portion of training data such that the trained model gives incorrect predictions when the specific trigger appears. Whi...
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Chapter and Conference Paper
Hydra Attention: Efficient Attention with Many Heads
While transformers have begun to dominate many tasks in vision, applying them to large images is still computationally difficult. A large reason for this is that self-attention scales quadratically with the nu...
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Chapter and Conference Paper
An Improved Lightweight Network Based on YOLOv5s for Object Detection in Autonomous Driving
Object detection with high accuracy and fast inference speed based on camera sensors is important for autonomous driving. This paper develops a lightweight object detection network based on YOLOv5s which is on...
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Chapter and Conference Paper
RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network
Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Map** (SLAM). Taking the task as a point cloud retrieval probl...
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Chapter and Conference Paper
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning
This paper presents a transformer framework for few-shot learning, termed TransVLAD, with one focus showing the power of locally aggregated descriptors for few-shot learning. Our TransVLAD model is simple: a s...
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Chapter and Conference Paper
Fast Node Selection of Networked Radar Based on Transfer Reinforcement Learning
The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and...
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Chapter and Conference Paper
Auto-regressive Image Synthesis with Integrated Quantization
Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in condi...
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Chapter and Conference Paper
DeltaGAN: Towards Diverse Few-Shot Image Generation with Sample-Specific Delta
Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-the-art works have yielded impr...
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Chapter and Conference Paper
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and ai...
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Chapter and Conference Paper
Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable pro...
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Chapter and Conference Paper
Towards Better Generalization for Neural Network-Based SAT Solvers
Neural network (NN) has demonstrated its astonishing power in many data mining tasks. Recently, NN is adapted to the boolean satisfiability (SAT) problem as a solver, which is trained on a dataset containing t...
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Chapter and Conference Paper
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning
Recent contrastive based 3D action representation learning has made great progress. However, the strict positive/negative constraint is yet to be relaxed and the use of non-self positive is yet to be explored....
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Chapter and Conference Paper
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection
Few-shot object detection is based on the base set with abundant labeled samples to detect novel categories with scarce samples. The majority of former solutions are mainly based on meta-learning or transfer-l...
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Chapter and Conference Paper
Cross-Lingual Product Retrieval in E-Commerce Search
Cross-lingual product retrieval (CLPR) recalls semantically relevant products that match multilingual search queries. It plays a crucial role in E-commerce sites to serve cross-border customers. However, there...
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Chapter and Conference Paper
Frequency Domain Model Augmentation for Adversarial Attack
For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversa...
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Chapter and Conference Paper
MobiCFNet: A Lightweight Model for Cattle Face Recognition in Nature
In smart livestock, precision livestock systems require efficient and safe non-contact cattle identification methods in daily operation and management. In this paper, we focus on lightweight Convolutional Neur...
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Chapter and Conference Paper
NeuMesh: Learning Disentangled Neural Mesh-Based Implicit Field for Geometry and Texture Editing
Very recently neural implicit rendering techniques have been rapidly evolved and shown great advantages in novel view synthesis and 3D scene reconstruction. However, existing neural rendering methods for editi...
