388 citations · 669 across the 11 of their papers we have counts for
12 papers
Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park +2
Diffusion models have been shown to be capable of generating high-quality images, suggesting that they could contain meaningful internal representations. Unfortunately, the feature…
Shape-Guided Diffusion with Inside-Outside Attention
Dong Huk Park, Grace Luo, Clayton Toste +5
We introduce precise object silhouette as a new form of user control in text-to-image diffusion models, which we dub Shape-Guided Diffusion. Our training-free method uses an Inside…
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
Dongwoo Park, Jong-Min Lee
Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and quer…
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
Xihui Liu, Dong Huk Park, Samaneh Azadi +6
Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image. Recently, denoising diffusion probabilistic mode…
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
Josh Beal, Hao-Yu Wu, Dong Huk Park +2
Large-scale pretraining of visual representations has led to state-of-the-art performance on a range of benchmark computer vision tasks, yet the benefits of these techniques at ext…
Toward Transformer-Based Object Detection
Josh Beal, Eric Kim, Eric Tzeng +3
Transformers have become the dominant model in natural language processing, owing to their ability to pretrain on massive amounts of data, then transfer to smaller, more specific t…
Learning a Unified Embedding for Visual Search at Pinterest
Andrew Zhai, Hao-Yu Wu, Eric Tzeng +2
At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing o…
Robust Change Captioning
Dong Huk Park, Trevor Darrell, Anna Rohrbach
Describing what has changed in a scene can be useful to a user, but only if generated text focuses on what is semantically relevant. It is thus important to distinguish distractors…
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +4
Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attent…
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +4
Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to…
Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +3
Deep models are the defacto standard in visual decision models due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and…
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Akira Fukui, Dong Huk Park, Daylen Yang +3
Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks suc…