Search papers, labs, and topics across Lattice.
Multimodal unlearning could revolutionize how we handle sensitive data in AI, enabling targeted removal without sacrificing model performance.
Achieving over 42% recall in semantic video communication could redefine how we transmit meaning in bandwidth-limited networks.
Current MLLMs struggle with Bangla form comprehension, missing key granular details that could hinder their real-world application in low-resource languages.
TrajLoc achieves unprecedented trajectory adherence and visual fidelity in multi-object motion control, outperforming existing methods by isolating object trajectories with Gaussian heatmaps.
Zero-shot generation of 360 panoramas is now possible without the costly fine-tuning or optimization typically required, unlocking new creative potentials in image synthesis.
Synthesizing 48,000 interaction trajectories without human input enables a humanoid robot to learn complex loco-manipulation tasks effectively.
ForceBand transforms human muscle signals into precise force data, enabling robots to learn manipulation tasks with unprecedented accuracy.
GLACIER achieves high predictive accuracy while drastically cutting down the computational costs typically associated with multimodal molecular property prediction.
Massive multimodal models like Qwen and CLIP excel at information retrieval, but their sheer size makes them impractical – this workshop tackles the efficiency gap.
Despite matching or exceeding human expert performance on generating potential diagnoses, current MLLMs struggle to synthesize multimodal clinical evidence for final diagnosis, revealing a critical gap in their clinical reasoning abilities.
Object hallucination in MLLMs can be significantly reduced by simply masking salient visual features during contrastive decoding.
