activity
20222025
most citedGeneralized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection

9 citations · 12 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2025

SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling

Shixuan Sun, Siyuan Liang, Jianjie Huang +2

Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Lan…

cs.CV20259 cited

Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection

Ruoyu Chen, Hua Zhang, Jingzhi Li +3

The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature sp…

cs.CV2024

ReCap: Better Gaussian Relighting with Cross-Environment Captures

Jingzhi Li, Zongwei Wu, Eduard Zamfir +1

Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement. Due to the albedo-lighting ambiguity, existing methods often fall s…

cs.CV2024

Interpreting Object-level Foundation Models via Visual Precision Search

Ruoyu Chen, Siyuan Liang, Jingzhi Li +5

Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. Howev…

cs.CV20242 cited

Logit Standardization in Knowledge Distillation

Shangquan Sun, Wenqi Ren, Jingzhi Li +2

Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function. However, the assumption of a shared temperat…

cs.CV20221 cited

A Large-scale Multiple-objective Method for Black-box Attack against Object Detection

Siyuan Liang, Longkang Li, Yanbo Fan +4

Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model info…