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Found 18 papers, 10 papers with code
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes
Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning.
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
None of the LMMs have yet comprehensively unified both types of tasks within a single model, as seen in Large Language Models in the natural language processing field.
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios.
Self-Supervised Representation Learning from Arbitrary Scenarios
To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution.
Mitigating Hallucination in Visual Language Models with Visual Supervision
Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses that apparently contradict to the image content occasionally.
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Current LVLMs are predominantly constrained to locate a single, pre-existing object.
Efficient Masked Autoencoders with Self-Consistency
Besides, we design the self-consistency learning to further maintain the consistency of predictions of overlapping masked patches among parts.
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling.
Masked Contrastive Pre-Training for Efficient Video-Text Retrieval
Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency.
Ranked #40 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification
PASS learns to match the output of the local views and global views on the same [PART].
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection
The decision boundary between any two categories is the angular bisector of their weight vectors.
DPT: Deformable Patch-based Transformer for Visual Recognition
To address this problem, we propose a new Deformable Patch (DePatch) module which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches.
Ranked #17 on
Semantic Segmentation
on DensePASS
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
Adaptive Class Suppression Loss for Long-Tail Object Detection
To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies.
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection.
Ranked #5 on
Object Detection
on PASCAL VOC 2007
