10 papers
Controlling Embedding Spaces with Text-Conditioned Transformations
Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani +2
Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification. These embeddings compre…
Seeing Through Words: Controlling Visual Retrieval Quality with Language Models
Jianglin Lu, Simon Jenni, Kushal Kafle +3
Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios it is often challenged by short and underspecified user queries. Such queries…
RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward
Qiucheng Wu, Jing Shi, Simon Jenni +4
Recent advances in multimodal large language models (MLLMs) have shown great potential for extending vision-language reasoning to professional tool-based image editing, enabling in…
CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning
Qihua Dong, Luis Figueroa, Handong Zhao +5
Referring Expression Comprehension and Segmentation are critical tasks for assessing the integration of language understanding and image comprehension, serving as benchmarks for Mu…
More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models
Hoang Anh Just, Yifei Fan, Handong Zhao +6
Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. How…
Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensembles
Eric Slyman, Mehrab Tanjim, Kushal Kafle +1
Multimodal large language models (MLLMs) are increasingly used to evaluate text-to-image (TTI) generation systems, providing automated judgments based on visual and textual context…