6 citations · 8 across the 13 of their papers we have counts for
5 papers · 1 filter
Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation
Miao Rang, Zhenni Bi, Hang Zhou +6
The rapid advancement of large language models (LLMs) has significantly advanced the capabilities of artificial intelligence across various domains. However, their massive scale an…
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Jianyuan Guo, Hanting Chen, Chengcheng Wang +3
Recent advancements in large language models have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating an…
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Jialong Zuo, Hanyu Zhou, Ying Nie +5
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts…
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models
Ying Nie, Wei He, Kai Han +4
Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the exis…
Towards Higher Ranks via Adversarial Weight Pruning
Yuchuan Tian, Hanting Chen, Tianyu Guo +2
Convolutional Neural Networks (CNNs) are hard to deploy on edge devices due to its high computation and storage complexities. As a common practice for model compression, network pr…