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Found 7 papers, 5 papers with code
Language-Image Models with 3D Understanding
Our experiments on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by 21. 3 points of AP-BEV on the Talk2Car dataset for 3D grounded reasoning and 17. 7 points on the DriveLM dataset for complex reasoning about driving scenarios, respectively.
Language-conditioned Detection Transformer
We use this detector to pseudo-label images with image-level labels.
Long-tail Detection with Effective Class-Margins
Large-scale object detection and instance segmentation face a severe data imbalance.
PartDistillation: Learning Parts From Instance Segmentation
PartDistillation transfers the part information of an instance segmentation model into a part segmentation model through self-supervised self-training on a large dataset.
NMS Strikes Back
Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50. 2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting.
Ranked #2 on
Object Detection
on COCO-O
(using extra training data)
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
With our novel learning objective, our framework can learn high-level semantic concepts.
Ranked #3 on
Unsupervised Semantic Segmentation
on COCO-Stuff-171
On the Efficacy of Knowledge Distillation
In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures.
