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Found 16 papers, 3 papers with code
CoLLM: A Large Language Model for Composed Image Retrieval
Experimental results demonstrate that CoLLM achieves state-of-the-art performance across multiple CIR benchmarks and settings.
Ranked #1 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRR
M-LLM Based Video Frame Selection for Efficient Video Understanding
Empirical results show that the proposed M-LLM video frame selector improves the performances various downstream video Large Language Model (video-LLM) across medium (ActivityNet, NExT-QA) and long (EgoSchema, LongVideoBench) context video question answering benchmarks.
Bringing Multimodality to Amazon Visual Search System
Through extensive experiments, we show that this change leads to a substantial improvement to the image to image matching problem.
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models
As the pre-trained model is fine-tuned, earlier checkpoints synthesize images with low subject fidelity but high prompt fidelity and diversity.
X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs
In this work, we focus on enhancing the visual representations for MLLMs by combining high-frequency and detailed visual representations, obtained through masked image modeling (MIM), with semantically-enriched low-frequency representations captured by CL.
Open Vocabulary Multi-Label Video Classification
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmentation.
VidLA: Video-Language Alignment at Scale
To effectively address this limitation, we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner, accounting for the temporally hierarchical nature of videos.
UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance
In this paper, we push forward the state-of-the-art performance of unsupervised MOT methods by proposing UnsMOT, a novel framework that explicitly combines the appearance and motion features of objects with geometric information to provide more accurate tracking.
SurveyLM: A platform to explore emerging value perspectives in augmented language models' behaviors
This white paper presents our work on SurveyLM, a platform for analyzing augmented language models' (ALMs) emergent alignment behaviors through their dynamically evolving attitude and value perspectives in complex social contexts.
Multi-modal Alignment using Representation Codebook
Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion.
Vision-Language Pre-Training with Triple Contrastive Learning
Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning.
Ranked #3 on
Zero-Shot Cross-Modal Retrieval
on COCO 2014
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection
Keypoint detection plays an important role in a wide range of applications.
SLADE: A Self-Training Framework For Distance Metric Learning
We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data.
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation
Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources.
Fashion Outfit Complementary Item Retrieval
We evaluate our method on the outfit compatibility, FITB and new retrieval tasks.
Searching for Apparel Products from Images in the Wild
In particular, Instagram and Twitter influencers often provide images of themselves wearing different outfits and their followers are often inspired to buy similar clothes. We propose a system to automatically find the closest visually similar clothes in the online Catalog (street-to-shop searching).
