activity
20162024
most citedSPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

25 citations · 84 across the 27 of their papers we have counts for

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21 papers · 1 filter

cs.CV2024

Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation

Ruijie Xu, Chuyu Zhang, Hui Ren +1

We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes. Existing work proposes an online poi…

cs.CV2024

From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models

Rongjie Li, Songyang Zhang, Dahua Lin +2

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Despite recent advancements, existing methods st…

cs.CV2024

Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

Rongjie Li, Yu Wu, Xuming He

Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improvin…

cs.CV2024

DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation

Zeeshan Hayder, Xuming He

Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relati…

cs.CV2024

SGTR+: End-to-end Scene Graph Generation with Transformer

Rongjie Li, Songyang Zhang, Xuming He

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one…

cs.CV2024

POT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering

Chuyu Zhang, Hui Ren, Xuming He

Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant pro…