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Found 28 papers, 13 papers with code
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
PointGST freezes the pre-trained model and introduces a lightweight, trainable Point Cloud Spectral Adapter (PCSA) to fine-tune parameters in the spectral domain.
3D Parameter-Efficient Fine-Tuning for Classification
3D Point Cloud Classification
+3
Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection
We present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains.
Exploring the Causality of End-to-End Autonomous Driving
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions.
SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation, which inspires the following core designs: a Simple Instance-aware Dense Sampling (SIDS) strategy is used to generate comprehensive dense pseudo-labels; the Geometry-aware Adaptive Weighting (GAW) loss dynamically modulates the importance of each pair between pseudo-label and corresponding prediction by leveraging the intricate geometric information of aerial objects; we treat aerial images as global layouts and explicitly build the many-to-many relationship between the sets of pseudo-labels and predictions via the proposed Noise-driven Global Consistency (NGC).
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
To achieve this goal, we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter, which generates a dynamic scale for each token, considering the token significance to the downstream task.
3D Parameter-Efficient Fine-Tuning for Classification
Transfer Learning
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
For such purpose, this paper presents an advanced sampler that achieves both high accuracy and efficiency.
Ranked #3 on
3D Part Segmentation
on ShapeNet-Part
PointMamba: A Simple State Space Model for Point Cloud Analysis
Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
Diffusion-based 3D Object Detection with Random Boxes
3D object detection is an essential task for achieving autonomous driving.
CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching
Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately.
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection
In this paper, we propose a framework named the Adaptive Distance Interval Separation Network (ADISN) that adopts a novel perspective on understanding depth maps, as a form that lies between LiDAR and images.
SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model
In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks.
Multi-Modal 3D Object Detection by Box Matching
Extensive experiments on the nuScenes dataset demonstrate that our method is much more stable in dealing with challenging cases such as asynchronous sensors, misaligned sensor placement, and degenerated camera images than existing fusion methods.
Ranked #48 on
3D Object Detection
on nuScenes
SOOD: Towards Semi-Supervised Oriented Object Detection
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem.
Ranked #1 on
Cross-Part Crowd Counting
on ShanghaiTech B
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance.
Repainting and Imitating Learning for Lane Detection
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network
In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference.
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
SGM3D: Stereo Guided Monocular 3D Object Detection
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
Self-Adaptive Partial Domain Adaptation
Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space.
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network
In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition.
Ranked #6 on
Action Recognition
on ActivityNet
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting
Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems.
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation
Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet.
Crowd Counting via Hierarchical Scale Recalibration Network
Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.
Enhanced 3D convolutional networks for crowd counting
Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting.
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
ACM-CNN consists of three types of modules: a coarse network, a fine network, and a smooth network.
Adversarial Category Alignment Network for Cross-domain Sentiment Classification
Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain.
