跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-09 23:50 · 全站最近入库：2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-09 00:51 — 2026-11-10 00:51（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-09 23:50最近发现新增版本：2026-11-09 13:22库内论文最新更新时间：2026-11-07 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

18,172 已收录论文15,506 AI 相关4,527 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 5 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 23:48（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1344122480cb6b47f28798298854e13eCVF · CVPR 2026

Beyond Weak Supervision: MLLMs-Guided Graded Knowledge Distillation for Unsupervised Camouflaged Object Detection

Chen, Huafeng, Zhu, Chenguang, Lyu, Yueming, Shan, Caifeng

原文摘要

Most Camouflaged Object Detection (COD) methods rely on costly pixel-level annotations. Recent studies have adopted unsupervised COD (UCOD) to eliminate labeling costs, but still suffer from two issues:1) insufficient supervision, leading to reliance on self-supervised backbone DINO and reduced model flexibility; and 2) ineffective use of pseudo-labels, which widens the performance gap with supervised methods and limits real-world applicability. In this paper, we propose a novel teacher-student framework for UCOD to address these two issues. To tackle the lack of supervision, we build a powerful teacher model by integrating Multimodal Large Language Models (MLLMs) and the Segment Anything Model (SAM) to generate high-quality pseudo-labels. However, the teacher model faces two challenges: 1) suboptimal performance of MLLMs in COD, and 2) cascading errors.To address these challenges, we first propose a Camouflaged-Aware Chain-of-Thought (CA-CoT) for MLLMs. CA-CoT guides MLLMs through step-by-step reasoning to simulate human perceptual processes, thereby enhancing their performance in COD.Subsequently, we design a Graded Mask Evaluator (GME) to mitigate cascading errors, which evaluates and grades the quality of masks generated by SAM, and then filters out the low-quality masks to provide more reliable supervision.To better leverage pseudo-labels, we propose Graded Knowledge Distillation (GKD), which adaptively enhances distillation at both image and pixel levels based on pseudo-label quality. Extensive experiments show that our method outperforms existing UCOD approaches by a large margin. Notably, our method also achieves good performance under zero-shot settings. Code will be released.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8bbbcf5def8e79cd8598fe337a5347c5CVF · CVPR 2026

3D-Object Perception Transformer (3PT)

Kalra, Agastya, Salzmann, Tim, Stoppi, Guy, Marin, Dmitrii, Agarwal, Rishav, Taamazyan, Vage, Bokeloh, Martin, Hinterstoisser, Stefan 等 13 位作者

原文摘要

Current approaches to zero-shot 3D-object perception typically rely on ensembles of frozen foundation models. This limits deep object understanding and cross-domain generalization, making performance inadequate for real-world deployment. The 3D-Object Perception Transformer (3PT) addresses this limitation by unifying detection, segmentation, and 6DoF pose estimation in a single framework, directly trained for 3D-object perception. Based on two large-scale trained transformers that specialize in 2D and 3D object-centric scene understanding respectively, 3PT continuously refines its object representations without depth input, enhancing 3D understanding by incorporating multi-view information. 3PT is the state-of-the-art for detection and pose estimation on the BOP benchmarks, often achieving double digit improvements, in many cases, outperforming non-zero-shot methods, and winning 7 of 11 tracks in the BOP-2025 challenge. 3PT surpasses task-specialized models for detection and pose estimation, often achieving double-digit percentage improvements on the diverse BOP-benchmarks, and in some cases outperforming non zero-shot methods. It also ranked first in 7 of 11 tracks at the BOP Challenge 2025. 3PT's high-accuracy and reliability is well-suited for practical industrial robotics applications such as bin picking and precise insertion. Project Page can be found at https://www.intrinsic.ai/publications/3pt-cvpr2026.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:abc12723d25e36c1f137724ef7cbc87bCVF · CVPR 2026

AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation

Guo, Wenxuan, Xu, Xiuwei, Liu, Yichen, Li, Xiangyu, Yin, Hang, Chen, Huangxing, Zheng, Wenzhao, Feng, Jianjiang 等 10 位作者

原文摘要

Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment. While state-of-the-art methods leverage the reasoning capabilities of Vision-Language Models (VLMs) for end-to-end action prediction, they often lack an explicit and explainable understanding of the relationships between the agent, the instruction, and the scene. Conversely, explicitly building a scene map for heuristic planning is intuitively appealing but relies on additional 3D sensors and hinders large-scale vision-language pre-training. To bridge this gap, we propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism, enabling it to understand the agent's state and task progress in a fully end-to-end and data-driven manner. Our approach features two key innovations: (1) a structural reasoning module that fosters spatial and task-oriented self-awareness, and (2) an automatic data engine with progress division for effective training. Extensive experiments on various datasets in Habitat simulator show our AwareVLN significantly outperforms previous state-of-the-art vision-language navigation methods. Project page: https://gwxuan.github.io/AwareVLN/.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b1c9b24c6ce323fff770ef31bb8867a3CVF · CVPR 2026

PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations

Yuan, Mingqi, Yu, Tao, Song, Haolin, Li, Bo, Jin, Xin, Chen, Hua, Zeng, Wenjun

原文摘要

Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of humanoid robots. To address this limitation, we propose **PvP**, a **P**roprioceptive-**P**rivileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states. PvP learns compact and task-relevant latent representations without requiring hand-crafted data augmentations, enabling faster and more stable policy learning. To support systematic evaluation, we develop **SRL4Humanoid**, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for humanoid robot learning. Extensive experiments on the LimX Oli robot across velocity tracking and motion imitation tasks demonstrate that PvP significantly improves sample efficiency and final performance compared to baseline SRL methods. Our study further provides practical insights into integrating SRL with RL for humanoid WBC, offering valuable guidance for data-efficient humanoid robot learning.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c96ff6a0f719e9e40177dd6aec930b9cCVF · CVPR 2026

Learning a Unified Latent Action Space from Videos with Action-centric Cycle Consistency

Chen, Guangyan, Shao, Qi, Cui, Te, Zhou, Zichen, Mao, Weixin, Yang, Luojie, Wang, Meiling, Yang, Yi 等 10 位作者

原文摘要

Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive video frames, and the tokenizer is trained to reconstruct future frames using current frames and the encoded latent actions. However, the unique pairing of successive frames permits future frame reconstruction with little understanding of transition dynamics, hindering the learning of semantically consistent latent actions. Moreover, the tokenizer typically allocates distinct latent action subsets to individual embodiments to accommodate heterogeneous morphologies, constraining knowledge transfer. To overcome such limitations, we propose the action-centric cycle consistency, aiming to establish a unified latent action space. Our method samples latent actions from the latent action space and decodes them with video frames to generate diverse subsequent frames, then enforces cycle consistency by predicting the sampled actions from both original and generated frames. Our concise method creates a challenging task that learns corresponding latent actions from current frames and diverse generated future frames, compelling the tokenizer to develop semantically consistent action representations. Additionally, sampled latent actions can be applied to video frames from distinct embodiments, facilitating the alignment of latent actions across embodiments. Experiments demonstrate that our approach achieves a 20.1% improvement over OpenVLA on the LIBERO benchmark and increases the average length from 3.27 to 3.93 on the CALVIN benchmark. In real-world experiments, our method maintains strong performance with a 44% improvement.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗