activity
20172024
most citedMulti-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

99 citations · 556 across the 39 of their papers we have counts for

collaborators

39 papers

cs.CV2024★ 3 cited

From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Chaochao Lu, Chen Qian, Guodong Zheng +33

Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…

cs.CV2024

Data-Free Generalized Zero-Shot Learning

Bowen Tang, Long Yan, Jing Zhang +3

Deep learning models have the ability to extract rich knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regardin…

cs.CV2023

Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation

Xiawei Li, Qingyuan Xu, Jing Zhang +4

3D point cloud semantic segmentation has a wide range of applications. Recently, weakly supervised point cloud segmentation methods have been proposed, aiming to alleviate the expe…

cs.CV2023★ 1 cited

MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception

Yiran Qin, Enshen Zhou, Qichang Liu +5

It is a long-lasting goal to design an embodied system that can solve long-horizon open-world tasks in human-like ways. However, existing approaches usually struggle with compound…

cs.CV2023★ 2 cited

EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion

Zehuan Huang, Hao Wen, Junting Dong +8

Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation i…

cs.CV2023★ 1 cited

ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models

Zhelun Shi, Zhipin Wang, Hongxing Fan +4

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. However, even though a list of b…