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20212026
most citedA Geometric Analysis of Neural Collapse with Unconstrained Features

43 citations · 58 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation

Tong Wang, Siwen Wang, Yaolei Qi +4

Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles)…

cs.CV2025

Improving Visual Discriminability of CLIP for Training-Free Open-Vocabulary Semantic Segmentation

Jinxin Zhou, Jiachen Jiang, Zhihui Zhu

Extending CLIP models to semantic segmentation remains challenging due to the misalignment between their image-level pre-training objectives and the pixel-level visual understandin…

cs.CV2025

Composed Object Retrieval: Object-level Retrieval via Composed Expressions

Tong Wang, Guanyu Yang, Nian Liu +4

Retrieving fine-grained visual content based on user intent remains a challenge in multimodal systems. Although current Composed Image Retrieval (CIR) methods combine reference ima…

cs.CV2025★ 1 cited

Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models

Jiachen Jiang, Jinxin Zhou, Bo Peng +2

Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent mod…

cs.CV2024

AdaContour: Adaptive Contour Descriptor with Hierarchical Representation

Tianyu Ding, Jinxin Zhou, Tianyi Chen +3

Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single gl…

cs.CV2023

DREAM: Diffusion Rectification and Estimation-Adaptive Models

Jinxin Zhou, Tianyu Ding, Tianyi Chen +4

We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantl…