6 citations · 15 across the 11 of their papers we have counts for
7 papers · 1 filter
Unified Text-Image Generation with Weakness-Targeted Post-Training
Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han +7
Unified multimodal generation architectures that jointly produce text and images have recently emerged as a promising direction for text-to-image (T2I) synthesis. However, many exi…
GenEval 2: Addressing Benchmark Drift in Text-to-Image Evaluation
Amita Kamath, Kai-Wei Chang, Ranjay Krishna +3
Automating Text-to-Image (T2I) model evaluation is challenging; a judge model must be used to score correctness, and test prompts must be selected to be challenging for current T2I…
Visual Representations inside the Language Model
Benlin Liu, Amita Kamath, Madeleine Grunde-McLaughlin +2
Despite interpretability work analyzing VIT encoders and transformer activations, we don't yet understand why Multimodal Language Models (MLMs) struggle on perception-heavy tasks.…
Matryoshka Query Transformer for Large Vision-Language Models
Wenbo Hu, Zi-Yi Dou, Liunian Harold Li +3
Large Vision-Language Models (LVLMs) typically encode an image into a fixed number of visual tokens (e.g., 576) and process these tokens with a language model. Despite their strong…
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models
Adyasha Maharana, Amita Kamath, Christopher Clark +2
As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support. Inconsistent AI models a…
Webly Supervised Concept Expansion for General Purpose Vision Models
Amita Kamath, Christopher Clark, Tanmay Gupta +3
General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both sk…
Towards General Purpose Vision Systems
Tanmay Gupta, Amita Kamath, Aniruddha Kembhavi +1
Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requi…