6 accepted papers
While prior research on Multimodal Large Language Model (MLLM) hallucinations has primarily examined cross-modal inconsistencies in natural images, hallucination over complex graph structures remains underexplored.Concurrently, there is a lack of robust evaluation for fine-grained reasoning integrat
Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL). This paper expands the pool of usable data for offline-to-online RL by leveraging abundant non-curated data that is reward-free, of mixed quality, and collected across multiple embodime…
While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode cor…
Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadverten…
We introduce a new category of generative autoencoders called automodulators. These networks can faithfully reproduce individual real-world input images like regular autoencoders, but also generate a fused sample from an arbitrary combination of several such images, allowing instantaneous "style-mix…
We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. This model uses pairs of images and poses, which are passed through an encoder-decoder model for disparity estimati…