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Works134 from public data
- GLaMM: Pixel Grounding Large Multimodal Model594
This work presents Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly in-tertwined with corresponding object segmentation masks and is flexible enough to accept both textual and optional visual prompts (region of interest) as input.
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- SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation200
A fast single-stage instance segmentation method that preserves instance-specific spatial information by separating mask prediction of an instance to different sub-regions of a detected bounding-box, leading to improved mask predictions and a mask alignment weighting loss and a feature alignment scheme to better correlate mask prediction with object detection.
- Person Image Synthesis via Denoising Diffusion Model186
This work shows how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution and introduces a ‘texture diffusion module’ based on cross-attention to accurately model the correspondences between appearance and pose information available in source and target images.
- 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization183
This work proposes a framework, called 3C-Net, which only requires video-level supervision (weak supervision) in the form of action category labels and the corresponding count to learn discriminative action features with enhanced localization capabilities.
- Foundational Models Defining a New Era in Vision: A Survey and Outlook172
A comprehensive review of emerging foundational models in computer vision, including typical architecture designs to combine different modalities, training objectives, pre-training datasets, fine-tuning mechanisms, and the common prompting patterns; textual, visual, and heterogeneous.
- Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes147
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other, and this work proposes to explicitly force the network to find the subtle differences among closely related classes.
- XrayGPT: Chest Radiographs Summarization using Large Medical Vision-Language Models145
This paper introduces XrayGPT, a conversational medical vision-language (VLMs) model that can analyze and answer open-ended questions about chest radiographs, and aligns both medical visual encoder with a fine-tuned LLM to possess visual conversation abilities, grounded in an understanding of radiographs and medical knowledge.
- Object Counting and Instance Segmentation With Image-Level Supervision136
This work is the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image- level supervised instance segmentation and outperforms existing methods, including those using instance-level supervision, on both datasets for common objects counting.
- Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery135
The proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery and achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset.
- LLM Post-Training: A Deep Dive Into Reasoning Large Language Models132
This survey provides a systematic exploration of post-training methodologies, analyzing their role in refining LLMs beyond pretraining, addressing key challenges such as catastrophic forgetting, reward hacking, and inference-time trade-offs.
- Remote Sensing Change Detection With Transformers Trained From Scratch129
This work develops an end-to-end CD approach with transformers that is trained from scratch and yet achieves state-of-the-art performance on five benchmarks, and introduces a change-enhanced feature fusion (CEFF) module to fuse the features from input image pairs by performing a per-channel re-weighting.
- ELGC-Net: Efficient Local–Global Context Aggregation for Remote Sensing Change Detection119
This work proposes an efficient change detection (CD) framework, efficient global and local context aggregation network (ELGC-Net), which leverages rich contextual information to precisely estimate change regions while reducing the model size, and introduces ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
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- Handwriting Transformers90
A novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local style patterns, and is the first to introduce a transformer- based network for styling handwritten text generation.
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- PSTR: End-to-End One-Step Person Search With Transformers88
The proposed PSTR is the first to propose an end-to-end one-step transformer-based person search framework that jointly performs person detection and re-identification (re-id) in a single architecture and sets a new state-of-the-art on both benchmarks.
- D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations69
A novel loss formulation is introduced, which jointly enhances the discriminability of latent embeddings and robustness of the output temporal class activations with respect to foreground-background noise caused by weak supervision, in a weakly-supervised temporal action localization framework, called D2-Net.
- PSC-Net: learning part spatial co-occurrence for occluded pedestrian detection61
The proposed PSC-Net contains a dedicated module that is designed to explicitly capture both inter and intra-part co-occurrence information of different pedestrian body parts through a graph convolutional network (GCN).
- MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT60
MobiLlama is a SLM design that initiates from a larger model and applies a careful parameter sharing scheme to reduce both the pre-training and the deployment cost, and strives to not only bridge the gap in open-source SLMs but also ensures full transparency.
- DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding57
A large multimodal model is introduced that is fine-tuned on the authors' reasoning dataset, demonstrating robust performance in complex driving scenarios, and various open-source and closed-source methods are benchmarked on this proposed dataset, systematically comparing their reasoning capabilities for autonomous driving tasks.
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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