Adapting Vision-Language Models (VLMs) to few-shot action recognition (FSAR) often trades accuracy for stability: task-specific gains can trigger catastrophic forgetting of domain-general knowledge and reduce inter-class margins. In few-shot episodes, each query is contrasted with only one positive class and a few negatives, so the text encoder sees limited prompt diversity and rarely observes hard counter-examples near decision boundaries. We propose Protect-to-Adapt (P2A), a parameter-efficient fine-tuning method with two complementary modules. Orthogonal Subspace Control (OSC) estimates a principal semantic subspace of the pre-trained backbone and constrains low-rank updates to its orthogonal complement, preserving domain-general semantics while allowing task-specific adaptation. Ranked Negative-prompt Curriculum (RNC) uses a large language model to generate verifier-filtered negative prompts with increasing difficulty. These class-specific hard counter-examples enlarge margins and sharpen decision boundaries under few-shot conditions. With only 2% of backbone parameters trainable, P2A achieves state-of-the-art performance on five FSAR benchmarks and substantially reduces catastrophic forgetting in a cross-dataset continual-learning setting where the model is adapted sequentially to multiple video datasets without replay.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on re-aligning an LLM with vision features to learn new special or object tokens for grounding, which may undermine the LLM's pretrained reasoning ability. In contrast, we propose **VGent**, a modular encoder-decoder architecture that explicitly disentangles high-level reasoning and low-level bounding box prediction. Specifically, a frozen MLLM serves as the encoder to provide untouched powerful reasoning capabilities, while a decoder takes high-quality boxes proposed by detectors as queries and selects target box(es) via cross-attending on encoder's hidden states. This design fully leverages advances in both object detection and MLLM, avoids the pitfalls of auto-regressive decoding, and enables fast inference. Moreover, it supports modular upgrades of both the encoder and decoder to benefit the whole system: we introduce (i) **QuadThinker**, an RL-based training paradigm for enhancing multi-target reasoning ability of the encoder; (ii) **mask-aware label** for resolving detection-segmentation ambiguity; and (iii) **global target recognition** to improve the recognition of all the targets which benefits the selection among augmented proposals. Experiments on multi-target visual grounding benchmarks show that VGent achieves a new state-of-the-art with **+20.6%** F1 improvement over prior methods, and further boosts gIoU by **+8.2%** and cIoU by **+5.8%** under visual reference challenges, while maintaining constant, fast inference latency.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution. To realize this, we introduce a novel instance-embedded representation based on deformable 3D Gaussians, which jointly encode radiance and semantic information and are learned directly from input RGB images and instance masks through differentiable rasterization.Furthermore, we introduce new mechanisms to calibrate per-Gaussian identities and resample Gaussians toward semantically active regions, ensuring consistent instance representations across space and time. Experiments on HyperNeRF and Neu3D datasets demonstrate that our method significantly outperforms state-of-the-art methods on novel-view panoptic segmentation and open-vocabulary 4D querying tasks.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表