9 papers indexed here
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This work proposes a large-scale multimodal large language model-based supervision generation framework that improves supervision diversity, fidelity, and semantic coverage, and introduces a granularity-aware text representation with separate CLS tokens for summary and detailed views.
We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D. Given a monocular video and an event-triggered prompt of the form"when X happens, do Y,"EgoPlay infers whether and w...
Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-context imitation methods discretize continuous actions and exacerbate the accumulation of early prediction errors through next-token prediction, limiting their generalizati...
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
B-MIM is introduced, a modification of the iBOT objective that stochastically reduces global semantic alignment to prioritize local patch reconstruction and suggests that reducing global semantic pressure during pretraining enhances generalization to intricate anatomical structures.
This work studies an inference-time substitution of the row-wise softmax in the final visual self-attention layers with the $\alpha$-entmax transform, applied across both the standard query-key attention and self-correlation variants.
GenView is presented, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics, and an adaptive view generation method that dynamically adjusts the noise level in sampling to ensure the preservation of essential semantic meani...
This work proposes HyperGS, a feedforward, optimization-free approach that directly predicts Gaussian representations from any video in a single forward pass, speeding up encoding and decoding by orders of magnitude while generalizing to out-of-distribution videos at higher resolutions.
DiSCO is proposed, a zero-shot, strictly black-box defense that operates entirely at the prompt level as a plug-and-play module, requiring no model retraining, fine-tuning, or access to model internals, and can be readily applied to any text-to-image system without necessitating any changes to the model itself.
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