Trending Research
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Based on this observation, we further sparsify delta parameters of multiple SFT homologous models with DARE and subsequently merge them into a single model by parameter averaging.
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Ranked #1 on
Zero-Shot Video Question Answer
on TGIF-QA
Emergence of Segmentation with Minimalistic White-Box Transformers
Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
CogVLM: Visual Expert for Pretrained Language Models
thudm/cogvlm
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We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #3 on
Visual Question Answering (VQA)
on CORE-MM
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
InRank: Incremental Low-Rank Learning
To remedy this, we design a new training algorithm Incremental Low-Rank Learning (InRank), which explicitly expresses cumulative weight updates as low-rank matrices while incrementally augmenting their ranks during training.
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
Semantic, instance, and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design.
Ranked #1 on
Panoptic Segmentation
on ScanNetV2
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis
Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios.
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability.
