SOTAVerified

Zero-shot Generalization

Papers

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Showing 151–175 of 572 papers

TitleStatusHype
S^3: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models—0
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono FailCode3
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
UTSD: Unified Time Series Diffusion Model—0
The Matrix: Infinite-Horizon World Generation with Real-Time Moving Control—0
COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detectionCode1
Collaborative Decoding Makes Visual Auto-Regressive Modeling EfficientCode2
vesselFM: A Foundation Model for Universal 3D Blood Vessel SegmentationCode2
Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models—0
Context-Aware Multimodal Pretraining—0
SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical SegmentationCode0
HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments—0
Scalable Autoregressive Monocular Depth Estimation—0
MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language ModelsCode0
Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language ModelsCode2
In the Era of Prompt Learning with Vision-Language Models—0
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting DiversityCode0
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuliCode0
ZIM: Zero-Shot Image Matting for AnythingCode3
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking—0
Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GR-MGAvg. sequence length4.04—Unverified
2MoDEAvg. sequence length4.01—Unverified
3RoboUniViewAvg. sequence length3.65—Unverified
43D Diffuser ActorAvg. sequence length3.27—Unverified
5GR-1Avg. sequence length3.06—Unverified