10 papers · 1 filter
DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents
Jiamian Wang, Ruiyi Zhang, Tong Yu +5
Recent methods train search agents via reinforcement learning from (question, answer, evidence) tuples without requiring expert trajectories. The tuples serve as the training envir…
Human-Aligned MLLM Judges for Fine-Grained Image Editing Evaluation: A Benchmark, Framework, and Analysis
Runzhou Liu, Hailey Weingord, Sejal Mittal +18
Evaluating image editing models remains challenging due to the coarse granularity and limited interpretability of traditional metrics, which often fail to capture aspects important…
Localizing Knowledge in Diffusion Transformers
Arman Zarei, Samyadeep Basu, Keivan Rezaei +3
Understanding how knowledge is distributed across the layers of generative models is crucial for improving interpretability, controllability, and adaptation. While prior work has e…
SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control
Arman Zarei, Samyadeep Basu, Mobina Pournemat +3
Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these model…
Improving Compositional Attribute Binding in Text-to-Image Generative Models via Enhanced Text Embeddings
Arman Zarei, Keivan Rezaei, Samyadeep Basu +4
Text-to-image diffusion-based generative models have the stunning ability to generate photo-realistic images and achieve state-of-the-art low FID scores on challenging image genera…
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP
Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi
Recent work has explored how individual components of the CLIP-ViT model contribute to the final representation by leveraging the shared image-text representation space of CLIP. Th…