activity
20192026
most citedContrastive Embedding for Generalized Zero-Shot Learning

22 citations · 25 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats

Bin Ren, Qi Ma, Yue Li +7

3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consume sampled observations of Ga…

cs.CV2026

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM

Amol Harsh, Zongyan Han, Jean Lahoud +5

Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to the referred 3D region, whil…

cs.CV2026

Audit After Segmentation: Reference-Free Mask Quality Assessment for Language-Referred Audio-Visual Segmentation

Jinxing Zhou, Yanghao Zhou, Yaoting Wang +5

Language-referred audio-visual segmentation (Ref-AVS) aims to segment target objects described by natural language by jointly reasoning over video, audio, and text. Beyond generati…

cs.CV2025

Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs

Dmitry Demidov, Zaigham Zaheer, Zongyan Han +2

Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for…

cs.CV2025

Bring Your Dreams to Life: Continual Text-to-Video Customization

Jiahua Dong, Xudong Wang, Wenqi Liang +7

Customized text-to-video generation (CTVG) has recently witnessed great progress in generating tailored videos from user-specific text. However, most CTVG methods assume that perso…

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…