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From the 1 of 66 linked papers with an AI index.

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20172025
most citedAutoNovel: Automatically Discovering and Learning Novel Visual Categories

151 citations · 317 across the 51 of their papers we have counts for

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Showing 2025 · cs.CVShow all

8 papers · 2 filters

cs.CV2025

ICE: Intrinsic Concept Extraction from a Single Image via Diffusion Models

Fernando Julio Cendra, Kai Han

The inherent ambiguity in defining visual concepts poses significant challenges for modern generative models, such as the diffusion-based Text-to-Image (T2I) models, in accurately…

cs.CV2025

Hyperbolic Category Discovery

Yuanpei Liu, Zhenqi He, Kai Han

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images,…

cs.CV2025

DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery

Yuanpei Liu, Kai Han

In this paper, we tackle the problem of Generalized Category Discovery (GCD). Given a dataset containing both labelled and unlabelled images, the objective is to categorize all ima…

cs.CV2025

v-CLR: View-Consistent Learning for Open-World Instance Segmentation

Chang-Bin Zhang, Jinhong Ni, Yujie Zhong +1

In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance…

cs.CV2025

ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval

Guanqi Zhan, Yuanpei Liu, Kai Han +2

The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-tr…

cs.CV2025

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31

Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…