Search Results for author:
Found 22 papers, 15 papers with code
FastMask: Segment Multi-scale Object Candidates in One Shot
Objects appear to scale differently in natural images.
Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN
This encourages the network to preserve the geometric structure in Euclidean space throughout the feature extraction hierarchy.
SaccadeNet: A Fast and Accurate Object Detector
Object detection is an essential step towards holistic scene understanding.
Ranked #194 on
Object Detection
on COCO test-dev
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.
Ranked #328 on
3D Object Detection
on nuScenes
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on
3D Object Detection
on KITTI Cars Hard val
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision.
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track
The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy.
FocalFormer3D: Focusing on Hard Instance for 3D Object Detection
For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
Vision Transformers Are Good Mask Auto-Labelers
We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations.
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
Augmenting pretrained language models (LMs) with a vision encoder (e. g., Flamingo) has obtained the state-of-the-art results in image-to-text generation.
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop.
Ranked #1 on
Prediction Of Occupancy Grid Maps
on Occ3D-nuScenes
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
Ranked #8 on
3D Object Detection
on nuScenes
ViR: Towards Efficient Vision Retention Backbones
Inspired by this trend, we propose a new class of computer vision models, dubbed Vision Retention Networks (ViR), with dual parallel and recurrent formulations, which strike an optimal balance between fast inference and parallel training with competitive performance.
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target.
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
With this in mind, we propose a simple yet effective approach to optimize VLMs in fine-grained understanding, achieving significant improvements on SPEC without compromising the zero-shot performance.
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection.
Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
We initially observed that the nuScenes dataset, characterized by relatively simple driving scenarios, leads to an under-utilization of perception information in end-to-end models incorporating ego status, such as the ego vehicle's velocity.
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties
Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models.
Fully Attentional Networks with Self-emerging Token Labeling
With the proposed STL framework, our best model based on FAN-L-Hybrid (77. 3M parameters) achieves 84. 8% Top-1 accuracy and 42. 1% mCE on ImageNet-1K and ImageNet-C, and sets a new state-of-the-art for ImageNet-A (46. 1%) and ImageNet-R (56. 6%) without using extra data, outperforming the original FAN counterpart by significant margins.
Ranked #16 on
Domain Generalization
on ImageNet-C
EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks
The advent of Large Models marks a new era in machine learning, significantly outperforming smaller models by leveraging vast datasets to capture and synthesize complex patterns.
Ranked #1 on
Speech Synthesis
on LibriTTS
What is Point Supervision Worth in Video Instance Segmentation?
Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos.
