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- LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention1,068
A zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge on traditional vision and language tasks, demonstrating the superior generalization capacity of the approach.
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- LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model774
This work augments LLaMA-Adapter by unlocking more learnable parameters and proposes an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
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- Personalize Segment Anything Model with One Shot400
A training-free Personalization approach for SAM, termed as PerSAM, which effectively adapt SAM for private use without any training, and can enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning.
- PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning293
This paper first collaborate CLIP and GPT to be a unified 3D open-world learner, named as Point-CLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection, demonstrating the generalization ability for unified 3D open-world learning.
- OneLLM: One Framework to Align All Modalities with Language286
This paper presents OneLLM, an MLLM that aligns eight modalities to language using a unified framework through a unified multimodal encoder and a progressive multimodal alignment pipeline.
- Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners255
CaFo, a Cascade of Foundation models that incorporates diverse prior knowledge of various pretraining paradigms for better few-shot learning is proposed that can fully unleash the potential of different pre-training methods and unify them to perform state-of-the-art for few- shot classification.
- Learning 3D Representations from 2D Pre-Trained Models via Image-to-Point Masked Autoencoders217
This paper proposes an alternative to obtain superior 3D representations from 2D pre-trained models via Image-to-Point Masked Autoencoders, named as I2P-MAE, which leverages the well learned 2D knowledge to guide 3D masked autoencoding, which reconstructs the masked point tokens with an encoder-decoder architecture.
- MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection216
This paper introduces the first DETR framework for Monocular DEtection with a depth-guided TRansformer, named MonoDETR, and modify the vanilla transformer to be depth-aware and guide the whole detection process by contextual depth cues.
- Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model177
This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks, employing the LLM model to generate Python programs that constitute a comprehensive perception, planning, and action loop for robotic tasks.
- ConvMAE: Masked Convolution Meets Masked Autoencoders164
The ConvMAE framework demonstrates that multi-scale hybrid convolution-transformer can learn more discriminative representations via the mask auto-encoding scheme, and adopts the masked convolution to prevent information leakage in the convolution blocks.
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- Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers147
This technical report introduces the Lumina-T2X family - a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions.
- Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement143
APE, an Adaptive Prior rEfinement method for CLIP’s pre-trained knowledge, which achieves superior accuracy with high computational efficiency and introduces two model variants, a training-free APE and a training-required APE-T.
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- The Argo: a high channel count recording system for neural recording in vivo122
The Argo system is the highest channel count in vivo neural recording system, supporting simultaneous recording from 65 536 channels, sampled at 32 kHz and 12-bit resolution, and helps pave the way for a future high data rate intracortical implant.
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- You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction79
This work proposes a lightweight fast Illumination Adaptive Transformer (IAT) which comprises two transformer-style branches: local estimation branch and global ISP branch which could also conduct both object detection and semantic segmentation under various light conditions.
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- Key technologies of human–computer interaction for immersive somatosensory interactive games using VR technology41
A naive Bayes gesture recognition algorithm based on joint vector, which has a high recognition rate and recognition speed, can effectively adapt to continuous data flow, has good compatibility with virtual reality applications, and significantly increases the user's sense of immersion in virtual scenes.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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