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Found 30 papers, 16 papers with code
Disentangle Your Dense Object Detector
Extensive experiments on MS COCO benchmark show that our approach can lead to 2. 0 mAP, 2. 4 mAP and 2. 2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
This paper aims to address the problem of supervised monocular depth estimation.
Ranked #20 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents.
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
Based on that, we further introduce T-Eval to evaluate the tool utilization capability step by step.
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Recently, AutoAlign presents a learnable paradigm in combining these two modalities for 3D object detection.
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm.
Ranked #14 on
3D Object Detection
on nuScenes Camera Only
Are We on the Right Way for Evaluating Large Vision-Language Models?
We evaluate 16 leading LVLMs on MMStar to assess their multi-modal capabilities, and on 7 benchmarks with the proposed metrics to investigate their data leakage and actual multi-modal gain.
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0. 311, an RMSE of 3. 79, and the inference time is 37$ms$ tested on the Raspberry Pi 4.
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations
To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
Towards Fine-grained Large Object Segmentation 1st Place Solution to 3D AI Challenge 2020 -- Instance Segmentation Track
This technical report introduces our solutions of Team 'FineGrainedSeg' for Instance Segmentation track in 3D AI Challenge 2020.
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Based on this, we develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain.
Learning from Noisy Data for Semi-Supervised 3D Object Detection
To this end, we propose NoiseDet, a simple yet effective framework for semi-supervised 3D object detection.
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
The visual prompts have provided an efficient manner in addressing visual cross-domain problems.
1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object Detection Track
In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track.
Channel Models and Coding Solutions for 1S1R Crossbar Resistive Memory with High Line Resistance
Simulations based on these models suggest that the bit-error rate of devices are highly non-uniform across the memory array.
AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection
This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner.
Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding.
Towards Model Generalization for Monocular 3D Object Detection
However, caused by severe domain gaps (e. g., the field of view (FOV), pixel size, and object size among datasets), Mono3D detectors have difficulty in generalization, leading to drastic performance degradation on unseen domains.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
In this paper, we propose a Multi-space Alignment Teacher-Student (MATS) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Geometric-space Aligned Student (GAS) model.
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View
Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation.
Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation
In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications.
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation
To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts.
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding
In this paper, we introduce LiDAR-LLM, which takes raw LiDAR data as input and harnesses the remarkable reasoning capabilities of LLMs to gain a comprehensive understanding of outdoor 3D scenes.
Stream Query Denoising for Vectorized HD Map Construction
This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction.
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track
In this report, we present our solution to the multi-task robustness track of the 1st Visual Continual Learning (VCL) Challenge at ICCV 2023 Workshop.
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection
Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming.
