38 citations · 215 across the 21 of their papers we have counts for
21 papers
COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation
Xinlong Zhang, Jia Wei, Xiaoyu Zhang +3
Achieving high-fidelity object-level control in Diffusion Transformers remains a significant challenge despite the introduction of structural priors like depth and Canny maps. Curr…
Score Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
Yun-Chen Cheng, Che-Yu Lin, Cheng-Lin Yang
Large language models hallucinate even when the answer lies within their parameters. While inference-time scaling can surface this latent knowledge, the most effective methods requ…
Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches
Yuanzheng Ci, Chen Lin, Lei Bai +1
Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for M…
UWC: Unit-wise Calibration Towards Rapid Network Compression
Chen Lin, Zheyang Li, Bo Peng +4
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance. Previous PTQ meth…
GLiT: Neural Architecture Search for Global and Local Image Transformer
Boyu Chen, Peixia Li, Chuming Li +6
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition. Recently, transformers without CNN-based backbones a…
BN-NAS: Neural Architecture Search with Batch Normalization
Boyu Chen, Peixia Li, Baopu Li +5
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS). BN-NAS can significantly reduce the time required b…