#ICLR2025 was a blast, great to talk with all of you about our works on hyperbolic vision-language models, a better way to do object detection, and brain-aligned image generation! Next destination: the Web Conference in Australia, happening now

Hyperbolic embeddings not only allow for an asymmetric modeling between vision and language, but open the door to models all sorts of compositions. This paper shows how image-box compositions form cool new hierarchies and strong vision-language models!

We find that hyperbolic vision-language models such as MERU: 1. exhibit spatial awareness (CLIP has no such awareness) 2. can better deal with ambiguity 3. are far better OOD discirminators Despite being trained on the same data, hyperbolic models really learn unique patterns!

Vision-language models benefit from hyperbolic embeddings for standard tasks, but did you know that hyperbolic vision-language models also have surprising properties? Our new #TMLR paper shows 3 intriguing properties. w/ @sarahibrahimi_ @GhadimiAtigMina @nannevn @marcelworring

Hyperbolic Random Forests at been accepted to #TMLR! Random forests split data hierarchically, making for strong links with hyperbolic space. We show how to generalize forests through horospheres, resulting in powerful machine learning models. Paper: openreview.net/pdf?id=pjKcIz…

8 "G3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding" by Yuan Wang et al. Hyperbolic learning allows for a better representation space to deal with the complex spatial and semantic relations in 3D visual grounding.

7 "HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation" by Zhiying Leng et al. 3D shape generation is strongly hierarchical. This paper shows how to use such structures in hyperbolic space to get impressive 3D shapes.

6 "Accept the Modality Gap: An Exploration in the Hyperbolic Space" by Sameera Ramasinghe et al. VLMs should operate in hyperbolic space! This paper shows how to preserve intra-modal hierarchies and cross-modal alignment in hyperbolic space.

5. "Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space" by Chengyang Hu et al. Hierarchical hyperbolic learning is beneficial in face recognition as well, especially with their proposed prototype learner.

4. "Hyperbolic Learning with Synthetic Captions for Open-World Detection" by Fanjie Kong et al. Open world detection is a hot new topic. This paper bootstraps VLMs with synthetic captions to get rich descriptions. Hyperbolic learning is a great tool to deal with noisy captions.