Search Results for author:
Found 7 papers, 6 papers with code
DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation
Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed.
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i. e., aligning point cloud representation to image and text embedding space individually.
Ranked #1 on
Zero-shot 3D classification
on Objaverse LVIS
(using extra training data)
ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation
We find that the architecture discrepancy between dense and sparse detectors leads to feature conflict, hampering the performance of one-decoder-layer detectors.
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field.
AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection
Critically, in FSDAOD, the data-scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment.
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data.
Understanding the robustness-accuracy tradeoff by rethinking robust fairness
Then, we explored the effect of another classic smoothing regularizer, namely, the maximum entropy (ME), and we have found ME can also help reduce both inter-class similarity and intra-class variance.
