arXiv:2606. 05115v1 Announce Type: cross Abstract: Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience.
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
The study demonstrates that large DINOv3 vision transformers, trained solely on images without any language supervision, can organize rendered words in their embedding space according to meaning. By deriving word representations from differences between word images and matched controls, the authors show that semantically related words cluster together, brand names group by industry, and country-capital pairs exhibit consistent vector offsets. These representations align with visual object categories and share structure with text and speech encoders, indicating that image-only training can impart both visual and semantic understanding of words.
The paper introduces GAVAGAI, a method that adds a referential alignment variable to vision‑language models by solving a small assignment problem via entropic optimal transport. By allowing words to be assigned to a special NOTHING column and enforcing a capacity constraint on regions, the approach improves word‑to‑picture accuracy and localization on the SAYCam infant video corpus. Experiments show that the alignment loss boosts performance over a standard next‑token captioning baseline, especially under high non‑reference rates.
arXiv:1805. 11546v3 Announce Type: replace-cross Abstract: We examine the benefits of visual context in training neural language models to perform next-word prediction.
The paper introduces an experiment where a small masked language model (DeBERTa) is initialized with visual embeddings for tokens that correspond to image regions, following St. Augustine’s ostensive definition of word learning. The visual initialization leaves a measurable imprint that persists through training, yet it does not improve performance on most BabyLM benchmarks that test abstract grammatical knowledge. However, the seeded models show a consistent advantage in zero‑shot object‑property tasks and in a custom Visual‑Property Swap benchmark that probes color, material, size, and shape knowledge, with the advantage confined to the seeded words and transferable to newly seeded words.
arXiv:2606. 29416v1 Announce Type: cross Abstract: Can a vision model truly see an object, or does it only fit surface-level visual cues?
The study investigates how unified vision‑language models (VLMs) can simultaneously support visual understanding and generation. Using controlled benchmarks (SmartWatch and modified CelebA) that pair VQA, captioning, and text‑to‑image tasks, the authors evaluate several LLM‑based architectures built on SigLIP and VQ‑VAE visual spaces. Results show that mixed training can improve both understanding and generation, but the gains depend on how well the visual input and output spaces are aligned; misaligned or distorted visual spaces can weaken or reverse these benefits. The paper also demonstrates that balancing data across tasks and controlling attribute frequencies can help recover underrepresented visual concepts, and that the transfer is driven more by the base language model’s learned relationships than by visual adapters.
arXiv:2610.34846v1 Announce Type: new Abstract: Learning an unfamiliar visual category requires more than adapting a classifier: the model must connect its visual evidence to a useful semantic name....
arXiv:2606. 27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models in other modalities remains underexplored.
Vision-language models (VLMs) are exact on small sets and drift into estimation on larger ones, a profile reminiscent of children before they master counting. Children cross the barrier with epistemic...
The paper introduces the Novel Visual References Dataset (NVRD), comprising 19,176 images of 90 new visual concepts with up to 20 perturbed versions each, designed to test how vision‑language models and humans map novel visual references to language. The authors evaluate three open‑source and two closed‑source VLMs against 2,400 human judgments, finding that models struggle to learn new concepts that contradict pre‑training knowledge and tend to overgeneralize labels to stimuli humans reject. NVRD is released as a benchmark for studying visual concept learning in both humans and machines.