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Found 13 papers, 4 papers with code
Event-enhanced Retrieval in Real-time Search
Furthermore, to strengthen the focus on critical event information in events, we include a decoder module after the document encoder, introduce a generative event triplet extraction scheme based on prompt-tuning, and correlate the events with query encoder optimization through comparative learning.
Exploration and Improvement of Nerf-based 3D Scene Editing Techniques
NeRF's high-quality scene synthesis capability was quickly accepted by scholars in the years after it was proposed, and significant progress has been made in 3D scene representation and synthesis.
DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection
Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i. e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation.
SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection
In this paper, we propose Semantic-Aware BEV Pooling (SA-BEVPool), which can filter out background information according to the semantic segmentation of image features and transform image features into semantic-aware BEV features.
Ranked #9 on
3D Object Detection
on nuScenes Camera Only
Event-Centric Query Expansion in Web Search
In search engines, query expansion (QE) is a crucial technique to improve search experience.
OcTr: Octree-based Transformer for 3D Object Detection
A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects.
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset
To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction.
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model.
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection
In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection.
Multi-scale fusion self attention mechanism
Based on the traditional attention mechanism, multi-scale fusion self attention extracts phrase information at different scales by setting convolution kernels at different levels, and calculates the corresponding attention matrix at different scales, so that the model can better extract phrase level information.
Cross Modification Attention Based Deliberation Model for Image Captioning
The conventional encoder-decoder framework for image captioning generally adopts a single-pass decoding process, which predicts the target descriptive sentence word by word in temporal order.
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects.
Ranked #4 on
3D Object Detection
on KITTI Cars Hard val
