Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks153 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.
- 443
- 417
- 362
- 324
- Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification287
This paper shows that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models.
- 281
- Mask-Guided Attention Network for Occluded Pedestrian Detection223
A novel mask-guided attention network is introduced that emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features and it is empirically demonstrated that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts.
- 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.
- 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.
- Spatio-temporal Relation Modeling for Few-shot Action Recognition159
A novel few-shot action recognition framework, STRM, which enhances class-specific feature discrim-inability while simultaneously learning higher-order temporal representations and introduces a query-class similarity classifier on the patch-level enriched features to enhance class- specific feature dis-criminability.
- 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.
- Class-Agnostic Object Detection with Multi-modal Transformer138
This paper advocates that existing methods lack a top-down supervision signal governed by human-understandable semantics and demonstrates that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap.
- Deep Contextual Attention for Human-Object Interaction Detection138
This work proposes a contextual attention framework for human-object interaction detection that leverages context by learning contextually-aware appearance features for human and object instances and adaptively selects relevant instance-centric context information to highlight image regions likely to contain human- object interactions.
- 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.
- 115
- 114
- Coloring Action Recognition in Still Images113
The experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors and that late fusion of color and shape information outperforms other approaches on action recognition.
- 97
- 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.
- 89
- 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.
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 2026-10-16. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.