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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.
- 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.
- Incorporating Convolution Designs into Visual Transformers613
A new Convolution-enhanced image Transformer (CeiT) is proposed which combines the advantages of CNNs in extracting low-level features, strengthening locality, and the disadvantages of Transformers in establishing long-range dependencies.
- Omni-Dimensional Dynamic Convolution479
Intriguingly, thanks to its improved feature learning ability, ODConv with even one single kernel can compete with or outperform existing dynamic convolution counterparts with multiple kernels, substantially reducing extra parameters.
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- MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning220
This paper proposes a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities, and yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems.
- Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification219
The effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter is explored, and a novel and effective prompting method, explicit \uline{c}ode-based \ULine{s}elf-\uline {v}erification~(CSV), is proposed to further boost the mathematical reasoning potential of GPN.
- Adversarial Robustness vs. Model Compression, or Both?186
It is found that weight pruning is essential for reducing the network model size in the adversarial setting; training a small model from scratch even with inherited initialization from the large model cannot achieve neither adversarial robustness nor high standard accuracy.
- 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.
- Deep Neural Network Compression With Single and Multiple Level Quantization125
This paper is the first to consider the network quantization from both width and depth level, and introduces incremental layer compensation to quantize layers iteratively which decreases the quantization loss in each iteration.
- An Algorithm–Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers102
A sparsity inheritance mechanism along with inherited dynamic pruning (IDP) to obtain a series of N:M sparse candidate Transformers rapidly and a model compression scheme is further proposed to significantly reduce the storage requirement for deployment.
- RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation101
This article introduces Ranker for top-k Recommendations (RecRanker), tailored for instruction tuning LLMs to serve as the Ranker for top-k Recommendations, and proposes a hybrid ranking method to enhance the model performance by ensembling these ranking tasks.
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- Deeply-Supervised Knowledge Synergy84
Deeply-supervised Knowledge Synergy is proposed, a new method aiming to train CNNs with improved generalization ability for image classification tasks without introducing extra computational cost during inference, and a novel synergy loss, which considers pairwise knowledge matching among all supervision branches.
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- Integrating Large Language Models Into Recommendation via Mutual Augmentation and Adaptive Aggregation62
A general and model-agnostic framework known as Large language models with mutual augmentation and adaptive aggregation for Recommendation (Llama4Rec) is introduced, aiming to bridge the gap via explicitly ensemble LLM and conventional recommendation model for more effective recommendation.
- Group R-CNN for Weakly Semi-supervised Object Detection with Points56
Group R-CNN significantly outperforms the prior method Point DETR by 3.9 mAP with 5% well-labeled images, which is the most challenging scenario, and is also the most effective method on the MS-COCO benchmark.
- HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions43
Harmonious Bottleneck on two Orthogonal dimensions (HBO), a novel architecture unit, specially tailored to boost the accuracy of extremely lightweight MobileNets at the level of less than 40 MFLOPs, is presented.
- Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoning42
This work proposes Step-Controlled DPO (SCDPO), a method for automatically providing stepwise error supervision by creating negative samples of mathematical reasoning rationales that start making errors at a specified step, which can better align the model to understand reasoning errors and output accurate reasoning steps.
- Pyramid Fusion Transformer for Semantic Segmentation39
This study finds that the per-mask classification decoder on top of a single-scale feature is not effective enough to extract reliable probabilities or masks, and proposes a transformer-based Pyramid Fusion Transformer (PFT) for per-mask approach semantic segmentation with multi-scale features.
- BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices31
This paper redesigns the dynamic resolution scheme adopted by mainstream MLLMs and implements system optimization for hardware-aware deployment to optimize model inference on mobile phones.
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- FeatAug-DETR: Enriching One-to-Many Matching for DETRs With Feature Augmentation28
This paper proposes two methods that realize one-to-many matching from a different perspective of augmenting images or image features and significantly accelerates DETR training and boosts detection performance while keeping the inference speed unchanged.
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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 2026-10-14. 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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