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Found 14 papers, 9 papers with code
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks.
Aligning and Prompting Everything All at Once for Universal Visual Perception
However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality interaction, which is not effective in prompting object detection and visual grounding.
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection
In this paper, we for the first time explore helpful multi-modal contextual knowledge to understand novel categories for open-vocabulary object detection (OVD).
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image.
Multi-modal Queried Object Detection in the Wild
To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed.
Ranked #1 on
Few-Shot Object Detection
on ODinW-35
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization
Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary.
Ranked #12 on
Open Vocabulary Object Detection
on LVIS v1.0
Efficient Decoder-free Object Detection with Transformers
A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference.
ARM: Any-Time Super-Resolution Method
To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets.
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue.
Ranked #11 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods.
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification
Several mainstream methods utilize extra cues (e. g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem.
Dive Deeper Into Box for Object Detection
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
