Search Results for author:
Found 29 papers, 11 papers with code
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation.
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning
Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings.
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving.
Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection
In light of this, we introduce SG2HOI+, a unified one-step model based on the Transformer architecture.
A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
Chain-of-thought reasoning, a cognitive process fundamental to human intelligence, has garnered significant attention in the realm of artificial intelligence and natural language processing.
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller.
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets.
X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification
In this paper, we propose to promote Instance-Level features to Identity-Level features by employing cross-attention to incorporate information from one image to another of the same identity, thus more unified and discriminative pedestrian information can be obtained.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning
In this paper, we introduce open-vocabulary scene graph generation, a novel, realistic and challenging setting in which a model is trained on a set of base object classes but is required to infer relations for unseen target object classes.
VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
In this paper, we solve the sparse KGC from these two motivations simultaneously and handle their respective drawbacks further, and propose a plug-and-play unified framework VEM$^2$L over sparse KGs.
A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets
Combined with auxiliary information exploiting modules, our methods achieve mAP of 89. 9% on DukeMTMC, where TOC, STS and SCP all contributed considerable performance improvements.
Ranked #1 on
Unsupervised Person Re-Identification
on MARS
A Free Lunch to Person Re-identification: Learning from Automatically Generated Noisy Tracklets
Extensive experiments on MARS with various manually generated noises show the effectiveness of the proposed framework.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the target domain.
Camera Bias Regularization for Person Re-identification
Due to the discrepancies between cameras caused by illumination, background, or viewpoint, the underlying difficulty for Re-ID is the camera bias problem, which leads to the large gap of within-identity features from different cameras.
Unsupervised Domain-adaptive Hash for Networks
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms.
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks.
Semantic Compositional Learning for Low-shot Scene Graph Generation
Scene graphs provide valuable information to many downstream tasks.
Exploiting Scene Graphs for Human-Object Interaction Detection
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects.
The Multi-Modal Video Reasoning and Analyzing Competition
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021.
A Computer-Aided Diagnosis System for Breast Pathology: A Deep Learning Approach with Model Interpretability from Pathological Perspective
Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer.
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Ranked #33 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features.
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network
We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.
Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer
Most of the characteristics learned by the deep learning models have summarized the detection rules that can be recognized by the experienced pathologists, whereas there are still some features may not be intuitive to domain experts but discriminative in classification for machines.
Deep Discrete Hashing with Self-supervised Pairwise Labels
2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way?
Deep Region Hashing for Efficient Large-scale Instance Search from Images
Specifically, DRH is an end-to-end deep neural network which consists of object proposal, feature extraction, and hash code generation.
