Matthieu Guillaumin

Vittorio Pippi, Matthieu Guillaumin, Silvia Cascianelli et al.

Large Multimodal Models (LMMs) are powerful tools that are capable of reasoning and understanding multimodal information beyond text and language. Despite their entrenched impact, the development of LMMs is hindered by the higher computational requirements compared to their unimodal counterparts. One of the main causes of this is the large amount of tokens needed to encode the visual input, which is especially evident for multi-image multimodal tasks. Recent approaches to reduce visual tokens depend on the visual encoder architecture, require fine-tuning the LLM to maintain the performance, and only consider single-image scenarios. To address these limitations, we propose ToFu, a visual encoder-agnostic, training-free Token Fusion strategy that combines redundant visual tokens of LMMs for high-resolution, multi-image, tasks. The core intuition behind our method is straightforward yet effective: preserve distinctive tokens while combining similar ones. We achieve this by sequentially examining visual tokens and deciding whether to merge them with others or keep them as separate entities. We validate our approach on the well-established LLaVA-Interleave Bench, which covers challenging multi-image tasks. In addition, we push to the extreme our method by testing it on a newly-created benchmark, ComPairs, focused on multi-image comparisons where a larger amount of images and visual tokens are inputted to the LMMs. Our extensive analysis, considering several LMM architectures, demonstrates the benefits of our approach both in terms of efficiency and performance gain.

Maximilian Jaritz, Matthieu Guillaumin, Sabine Sternig et al.

Multimodal retrieval methods have limitations in handling complex, compositional queries that require reasoning about the visual content of both the query and the retrieved entities. On the other hand, Large Multimodal Models (LMMs) can answer with language to more complex visual questions, but without the inherent ability to retrieve relevant entities to support their answers. We aim to address these limitations with UniCoRN, a Unified Commented Retrieval Network that combines the strengths of composed multimodal retrieval methods and generative language approaches, going beyond Retrieval-Augmented Generation (RAG). We introduce an entity adapter module to inject the retrieved multimodal entities back into the LMM, so it can attend to them while generating answers and comments. By keeping the base LMM frozen, UniCoRN preserves its original capabilities while being able to perform both retrieval and text generation tasks under a single integrated framework. To assess these new abilities, we introduce the Commented Retrieval task (CoR) and a corresponding dataset, with the goal of retrieving an image that accurately answers a given question and generate an additional textual response that provides further clarification and details about the visual information. We demonstrate the effectiveness of UniCoRN on several datasets showing improvements of +4.5% recall over the state of the art for composed multimodal retrieval and of +14.9% METEOR / +18.4% BEM over RAG for commenting in CoR.

Jasmine Collins, Shubham Goel, Kenan Deng et al.

We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.

Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari et al.

We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs). It is inspired by existing closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology. LS-CRF training requires only solving a set of independent regression problems, for which closed-form expression as well as efficient iterative solvers are available. This makes it orders of magnitude faster than conventional maximum likelihood learning for CRFs that require repeated runs of probabilistic inference. At the same time, the models learned by our method still allow for joint inference at test time. We apply LS-CRF to the task of semantic image segmentation, showing that it is highly efficient, even for loopy models where probabilistic inference is problematic. It allows the training of image segmentation models from significantly larger training sets than had been used previously. We demonstrate this on two new datasets that form a second contribution of this paper. They consist of over 180,000 images with figure-ground segmentation annotations. Our large-scale experiments show that the possibilities of CRF-based image segmentation are far from exhausted, indicating, for example, that semi-supervised learning and the use of non-linear predictors are promising directions for achieving higher segmentation accuracy in the future.