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Dequan Wang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.AI1
  • cs.CL1
same name
  • Dequan Wang — 14 papers, h 18
  • Dequan Wang — 6 papers, h 3
  • Dequan Wang — 5 papers, h 9
  • Dequan Wang — 5 papers, h 1
  • Dequan Wang — 5 papers, h 3
  • Dequan Wang — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedAn Extensible Framework for Open Heterogeneous Collaborative Perception

2 citations · 2 across the 2 of their papers we have counts for

collaborators

4 papers

cs.AI2024

Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions

Jin Gao, Lei Gan, Yuankai Li +2

Large multimodal models (LMMs) excel in adhering to human instructions. However, self-contradictory instructions may arise due to the increasing trend of multimodal interaction and…

cs.CL2024

Direct Preference Knowledge Distillation for Large Language Models

Yixing Li, Yuxian Gu, Li Dong +3

In the field of large language models (LLMs), Knowledge Distillation (KD) is a critical technique for transferring capabilities from teacher models to student models. However, exis…

cs.CV2024★ 2 cited

An Extensible Framework for Open Heterogeneous Collaborative Perception

Yifan Lu, Yue Hu, Yiqi Zhong +3

Collaborative perception aims to mitigate the limitations of single-agent perception, such as occlusions, by facilitating data exchange among multiple agents. However, most current…

cs.CV2023

BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference

Siqi Kou, Lei Gan, Dequan Wang +2

Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper samp…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.