5 citations · 6 across the 7 of their papers we have counts for
9 papers
CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning
Ankan Deria, Komal Kumar, Xilin He +4
Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive enc…
The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report
Bin Ren, Hang Guo, Yan Shu +60
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a…
RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion
Thao Nguyen, Jiaqi Ma, Fahad Shahbaz Khan +2
Precipitation nowcasting, predicting future radar echo sequences from current observations, is a critical yet challenging task due to the inherently chaotic and tightly coupled spa…
LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation
Xinyu Yan, Meijun Sun, Ge-Peng Ji +3
We present LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks. Our framework recasts DIS as an imag…
Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model
Dmitry Demidov, Zaigham Zaheer, Omkar Thawakar +2
Fine-grained image classification, the task of distinguishing between visually similar subcategories within a broader category (e.g., bird species, car models, flower types), is a…
Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Model
Shiming Chen, Bowen Duan, Salman Khan +1
Large-scale vision-language models (VLMs), such as CLIP, have achieved remarkable success in zero-shot learning (ZSL) by leveraging large-scale visual-text pair datasets. However,…
GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
Shiming Chen, Dingjie Fu, Salman Khan +1
Remarkable progress in zero-shot learning (ZSL) has been achieved using generative models. However, existing generative ZSL methods merely generate (imagine) the visual features fr…
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
Shuo Ye, Shiming Chen, Ruxin Wang +5
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categori…