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- Learning Efficient Convolutional Networks through Network Slimming2,909
The approach is called network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy.
- DSOD: Learning Deeply Supervised Object Detectors from Scratch646
Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch following the single-shot detection (SSD) framework, and one of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector.
- ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions452
This paper proposes to generalize the traditional Sign and PReLU functions to enable explicit learning of the distribution reshape and shift at near-zero extra cost and shows that the proposed ReActNet outperforms all the state-of-the-arts by a large margin.
- Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection243
This work represents each class concept by a semantic embedding learned from a large corpus of text and proposes to augment the embeddings with a dynamic relation graph, resulting in a few-shot detector that is robust and stable to the variation of shots of novel objects.
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- Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning210
This paper proposes to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model to control the extent of preserved transferability, and introduces an evolutionary search based method that is efficient to simultaneously locate the target layers and determine their individual learning rates.
- Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation175
This study proposes Nonuniform-to-Uniform Quantization (N2UQ), a method that can maintain the strong representation ability of nonuniform methods while being hardware-friendly and efficient as the uniform quantization for model inference.
- Soft Anchor-Point Object Detection168
This work boosts the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage, and proposes a simple yet effective training strategy with soft-weighted anchor points and soft-selected pyramid levels to address the false attention issue within each pyramid level and the feature selection issue across all the pyramid levels.
- TS$$^{2}$$C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection160
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C), developed based on two key properties of desirable bounding box: high purity and high completeness.
- MEAL: Multi-Model Ensemble via Adversarial Learning157
This paper proposes an adversarial-based learning strategy where a block-wise training loss is defined to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
- Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4148
26 guiding principles designed to streamline the process of querying and prompting large language models are introduced and hope that this work can provide a better guide for researchers working on the prompting of large language models.
- Weakly Supervised Dense Video Captioning142
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences, and proposes lexical fully convolutional neural networks with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels.
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- One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning129
Generalized LoRA is presented, an advanced approach for universal parameter-efficient fine-tuning tasks, which outperforms all previous methods in natural, specialized, and structured vision benchmarks, achieving superior accuracy with fewer parameters and computations.
- LLM360: Towards Fully Transparent Open-Source LLMs124
LLM360, an initiative to fully open-source LLMs, is presented, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community.
- Towards Instance-Level Image-To-Image Translation109
This paper presents a simple yet effective instance-aware image-to-image translation approach (INIT), which employs the fine-grained local (instance) and global styles to the target image spatially and collects a large-scale benchmark for the new instance-level translation task.
- Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena103
The Open-LLM-Leaderboard is introduced to track various LLMs' performance and reflect true capability of them, such as GPT-4o/4/3.5, Claude 3, Gemini, etc.
- Object Detection from Scratch with Deep Supervision103
Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch, is proposed based on the single-shot detection framework (SSD), and achieves consistently better results than the state-of-the-art methods with much more compact models.
- Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space96
This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework that can search a sub-structure from the original model end-to-end across multiple dimensions with state-of-the-art performance.
- SlimPajama-DC: Understanding Data Combinations for LLM Training88
The impacts of various data combinations on the pretraining of large language models using SlimPajama aims to understand the impacts of various data combinations and analyzes and discusses how global and local deduplications affect the performance of trained models.
- MoBiNet: A Mobile Binary Network for Image Classification77
A novel neural network architecture, namely MoBi- Net - Mobile Binary Network in which skip connections are manipulated to prevent information loss and vanishing gradient, thus facilitate the training process and results in an effectively small model while keeping the accuracy comparable to existing ones.
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- A Fast Knowledge Distillation Framework for Visual Recognition70
A Fast Knowledge Distillation (FKD) framework is presented that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, while training faster than ReLabel since no post-processes such as RoI align and softmax operations are used.
- Solving Missing-Annotation Object Detection with Background Recalibration Loss63
A superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image and is built on the one-stage detector which is faster and lighter.
- Initializing Models with Larger Ones51
Weight selection is introduced, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model, which enables the transfer of knowledge from pretrained weights to smaller models.
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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