arXiv AI June 16, 2026 By WonJun Moon, Jae-Pil Heo

Selective Synergistic Learning for Video Object-Centric Learning

arXiv:2606. 15527v1 Announce Type: cross Abstract: Typical video object-centric learning (VOCL) approaches employ slot-based frameworks that rely on reconstruction-driven encoder-decoder architectures, where learning is mediated by two spatial maps: attention maps from the encoder and object maps from the decoder.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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arXiv Machine Learning

Jul 2

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.

By Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, Kun Zhang

multimodal safety

Hugging Face Trending Papers

Aug 3

CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models

In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.

llms multimodal benchmarks safety

arXiv AI

Jul 16

ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding

arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.

By Kai Chen, Ming Dai, Wenxuan Cheng, Wankou Yang

multimodal benchmarks

arXiv AI

Jun 2

Improving Visual Representation Alignment Generation with GRPO

arXiv:2606. 00583v1 Announce Type: cross Abstract: Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between generative and discriminative representations.

By Shentong Mo, Sukmin Yun

llms rag diffusion safety

arXiv AI

Aug 5

Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models

arXiv:2608. 03112v1 Announce Type: cross Abstract: Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge devices and in real-time surveillance applications.

By Paribesh Regmi, Qingshuang Chen, Chi Zhang, Heba Aly, Yelin Kim, Hongda Mao

llms rag fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 30

MotionAtlas: Detailed Region Captioning for Motion-Centric Videos

arXiv:2606. 29531v1 Announce Type: cross Abstract: We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a family of powerful Video-MLLMs.

By Weisong Liu, Haochen Wang, Kuan Gao, Yuhao Wang, Yikang Zhou, Zhongwei Ren, Jacky Mai, Anna Wang, Yanwei Li, Jason Li, Zhaoxiang Zhang

benchmarks safety data-engineering

Related stories

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models

ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding

Improving Visual Representation Alignment Generation with GRPO

Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models

MotionAtlas: Detailed Region Captioning for Motion-Centric Videos